73 results on '"Costa Lda F"'
Search Results
2. Topic segmentation via community detection in complex networks.
- Author
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de Arruda HF, Costa Lda F, and Amancio DR
- Subjects
- Semantics, Computer Simulation, Information Systems
- Abstract
Many real systems have been modeled in terms of network concepts, and written texts are a particular example of information networks. In recent years, the use of network methods to analyze language has allowed the discovery of several interesting effects, including the proposition of novel models to explain the emergence of fundamental universal patterns. While syntactical networks, one of the most prevalent networked models of written texts, display both scale-free and small-world properties, such a representation fails in capturing other textual features, such as the organization in topics or subjects. We propose a novel network representation whose main purpose is to capture the semantical relationships of words in a simple way. To do so, we link all words co-occurring in the same semantic context, which is defined in a threefold way. We show that the proposed representations favor the emergence of communities of semantically related words, and this feature may be used to identify relevant topics. The proposed methodology to detect topics was applied to segment selected Wikipedia articles. We found that, in general, our methods outperform traditional bag-of-words representations, which suggests that a high-level textual representation may be useful to study the semantical features of texts.
- Published
- 2016
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3. Temporal modulation of collective cell behavior controls vascular network topology.
- Author
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Kur E, Kim J, Tata A, Comin CH, Harrington KI, Costa Lda F, Bentley K, and Gu C
- Subjects
- Animals, Computer Simulation, Mice, Inbred C57BL, Mice, Knockout, Optical Imaging, Cell Proliferation, Endothelial Cells physiology, Neovascularization, Physiologic, Receptors, Notch metabolism, Vascular Endothelial Growth Factor A metabolism
- Abstract
Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology.
- Published
- 2016
- Full Text
- View/download PDF
4. Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants.
- Author
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Moreira-Filho CA, Bando SY, Bertonha FB, Silva FN, Costa Lda F, Ferreira LR, Furlanetto G, Chacur P, Zerbini MC, and Carneiro-Sampaio M
- Subjects
- Down Syndrome immunology, Down Syndrome pathology, Female, Gene Expression Profiling, High-Throughput Nucleotide Sequencing methods, Humans, Infant, Male, Prognosis, RNA, Messenger genetics, Real-Time Polymerase Chain Reaction, Reverse Transcriptase Polymerase Chain Reaction, Thymus Gland immunology, Thymus Gland pathology, Biomarkers analysis, Down Syndrome genetics, Gene Expression Regulation, Gene Regulatory Networks, Genomics methods, MicroRNAs genetics, Thymus Gland metabolism
- Abstract
Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue--obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT)--and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the "canonical" way of thymus functioning. Conversely, DS networks represent a "non-canonical" way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes.
- Published
- 2016
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5. Thermodynamic characterization of networks using graph polynomials.
- Author
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Ye C, Comin CH, Peron TK, Silva FN, Rodrigues FA, Costa Lda F, Torsello A, and Hancock ER
- Abstract
In this paper, we present a method for characterizing the evolution of time-varying complex networks by adopting a thermodynamic representation of network structure computed from a polynomial (or algebraic) characterization of graph structure. Commencing from a representation of graph structure based on a characteristic polynomial computed from the normalized Laplacian matrix, we show how the polynomial is linked to the Boltzmann partition function of a network. This allows us to compute a number of thermodynamic quantities for the network, including the average energy and entropy. Assuming that the system does not change volume, we can also compute the temperature, defined as the rate of change of entropy with energy. All three thermodynamic variables can be approximated using low-order Taylor series that can be computed using the traces of powers of the Laplacian matrix, avoiding explicit computation of the normalized Laplacian spectrum. These polynomial approximations allow a smoothed representation of the evolution of networks to be constructed in the thermodynamic space spanned by entropy, energy, and temperature. We show how these thermodynamic variables can be computed in terms of simple network characteristics, e.g., the total number of nodes and node degree statistics for nodes connected by edges. We apply the resulting thermodynamic characterization to real-world time-varying networks representing complex systems in the financial and biological domains. The study demonstrates that the method provides an efficient tool for detecting abrupt changes and characterizing different stages in network evolution.
- Published
- 2015
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6. Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures.
- Author
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Moreira-Filho CA, Bando SY, Bertonha FB, Iamashita P, Silva FN, Costa Lda F, Silva AV, Castro LH, and Wen HT
- Subjects
- Adolescent, Adult, Age of Onset, CA3 Region, Hippocampal metabolism, CA3 Region, Hippocampal pathology, Epilepsy, Temporal Lobe pathology, Epilepsy, Temporal Lobe surgery, Female, Gene Expression Regulation, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Epilepsy, Temporal Lobe genetics, Gene Expression Profiling methods, Gene Regulatory Networks, Seizures, Febrile genetics
- Abstract
Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to generate adaptive mechanisms, what has implications for epilepsy management and drug discovery.
- Published
- 2015
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7. A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks.
- Author
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de Arruda HF, Comin CH, Miazaki M, Viana MP, and Costa Lda F
- Subjects
- Animals, Humans, Models, Neurological, Brain cytology, Brain metabolism, Computational Biology, Gene Expression physiology, Neurons physiology, Nonlinear Dynamics
- Abstract
Background: A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings., New Method: In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model., Results: As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression., Comparison With Existing Methods: To our best understanding, there are no similar analyses to compare with., Conclusions: A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity., (Copyright © 2015. Published by Elsevier B.V.)
- Published
- 2015
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8. Surface Morphology and Structural Modification Induced by Femtosecond Pulses in Hydrogenated Amorphous Silicon Films.
- Author
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Almeida GF, Cardoso MR, Aoki PH, Lima JJ Jr, Costa Lda F, Rodrigues CA, Constantino CJ, and Mendoncal CR
- Abstract
This work investigates the modification, resulting from fs-laser irradiation (150 fs, 775 nm and 1 kHz), on the structure and surface morphology of hydrogenated amorphous silicon (a-Si:H) thin films. The sample morphology was studied by performing a statistical analyzes of atomic force microscopy images, using a specially developed software that identifies and characterizes the domains (spikes) produced by the laser irradiation. For a fluence of 3.1 MJ/m2, we observed formation of spikes with smaller average height distribution, centered at around 15 nm, while for fluencies higher than 3.7 MJ/m2 aggregation of the produced spikes dominates the sample morphology. On the other hand, Raman spectroscopy revealed that a higher crystalline fraction (73%) is obtained for higher fluences (> 3.1 MJ/m2), which is accompanied by a decrease in the size of the produced crystals. Therefore, such results indicate that there is a trade-off between the spike distribution, crystallization fraction and size of the nanocrystals attained by laser irradiation, which has to be taken into account when using such approach for the development of devices.
- Published
- 2015
- Full Text
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9. An image processing approach to analyze morphological features of microscopic images of muscle fibers.
- Author
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Comin CH, Xu X, Wang Y, Costa Lda F, and Yang Z
- Subjects
- Animals, Cells, Cultured, Male, Mice, Mice, Inbred C57BL, Reproducibility of Results, Sensitivity and Specificity, Algorithms, Image Enhancement methods, Image Interpretation, Computer-Assisted methods, Microscopy methods, Muscle Fibers, Skeletal cytology, Pattern Recognition, Automated methods
- Abstract
We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness., (Copyright © 2014 Elsevier Ltd. All rights reserved.)
- Published
- 2014
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10. Entropy of weighted recurrence plots.
- Author
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Eroglu D, Peron TK, Marwan N, Rodrigues FA, Costa Lda F, Sebek M, Kiss IZ, and Kurths J
- Abstract
The Shannon entropy of a time series is a standard measure to assess the complexity of a dynamical process and can be used to quantify transitions between different dynamical regimes. An alternative way of quantifying complexity is based on state recurrences, such as those available in recurrence quantification analysis. Although varying definitions for recurrence-based entropies have been suggested so far, for some cases they reveal inconsistent results. Here we suggest a method based on weighted recurrence plots and show that the associated Shannon entropy is positively correlated with the largest Lyapunov exponent. We demonstrate the potential on a prototypical example as well as on experimental data of a chemical experiment.
- Published
- 2014
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11. Sensory-related neural activity regulates the structure of vascular networks in the cerebral cortex.
- Author
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Lacoste B, Comin CH, Ben-Zvi A, Kaeser PS, Xu X, Costa Lda F, and Gu C
- Subjects
- Age Factors, Animals, Animals, Newborn, Cell Proliferation, GTP-Binding Proteins genetics, GTP-Binding Proteins metabolism, Gene Expression Regulation, Developmental genetics, In Vitro Techniques, Luminescent Proteins genetics, Luminescent Proteins metabolism, Mice, Mice, Transgenic, Phosphopyruvate Hydratase metabolism, Physical Stimulation, Receptor, TIE-2 genetics, Receptor, TIE-2 metabolism, Vibrissae injuries, rab3 GTP-Binding Proteins genetics, Afferent Pathways physiology, Cerebral Cortex cytology, Cerebral Cortex physiology, Cerebrovascular Circulation physiology, Sensory Receptor Cells physiology, Vibrissae physiology
- Abstract
Neurovascular interactions are essential for proper brain function. While the effect of neural activity on cerebral blood flow has been extensively studied, whether or not neural activity influences vascular patterning remains elusive. Here, we demonstrate that neural activity promotes the formation of vascular networks in the early postnatal mouse barrel cortex. Using a combination of genetics, imaging, and computational tools to allow simultaneous analysis of neuronal and vascular components, we found that vascular density and branching were decreased in the barrel cortex when sensory input was reduced by either a complete deafferentation, a genetic impairment of neurotransmitter release at thalamocortical synapses, or a selective reduction of sensory-related neural activity by whisker plucking. In contrast, enhancement of neural activity by whisker stimulation led to an increase in vascular density and branching. The finding that neural activity is necessary and sufficient to trigger alterations of vascular networks reveals an important feature of neurovascular interactions., (Copyright © 2014 Elsevier Inc. All rights reserved.)
- Published
- 2014
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12. Role of centrality for the identification of influential spreaders in complex networks.
- Author
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de Arruda GF, Barbieri AL, Rodríguez PM, Rodrigues FA, Moreno Y, and Costa Lda F
- Subjects
- Computer Simulation, Databases, Factual, Disease Transmission, Infectious, England, Germany, Japan, Probability, Social Behavior, Transportation, United States, Communication, Epidemics, Models, Theoretical
- Abstract
The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.
- Published
- 2014
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13. Approximate von Neumann entropy for directed graphs.
- Author
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Ye C, Wilson RC, Comin CH, Costa Lda F, and Hancock ER
- Subjects
- Animals, Computer Simulation, Entropy, Humans, Algorithms, Metabolic Networks and Pathways physiology, Models, Biological, Models, Statistical, Protein Interaction Mapping methods, Proteome metabolism
- Abstract
In this paper, we develop an entropy measure for assessing the structural complexity of directed graphs. Although there are many existing alternative measures for quantifying the structural properties of undirected graphs, there are relatively few corresponding measures for directed graphs. To fill this gap in the literature, we explore an alternative technique that is applicable to directed graphs. We commence by using Chung's generalization of the Laplacian of a directed graph to extend the computation of von Neumann entropy from undirected to directed graphs. We provide a simplified form of the entropy which can be expressed in terms of simple node in-degree and out-degree statistics. Moreover, we find approximate forms of the von Neumann entropy that apply to both weakly and strongly directed graphs, and that can be used to characterize network structure. We illustrate the usefulness of these simplified entropy forms defined in this paper on both artificial and real-world data sets, including structures from protein databases and high energy physics theory citation networks.
- Published
- 2014
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14. A systematic comparison of supervised classifiers.
- Author
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Amancio DR, Comin CH, Casanova D, Travieso G, Bruno OM, Rodrigues FA, and Costa Lda F
- Subjects
- Data Interpretation, Statistical, ROC Curve, Support Vector Machine
- Abstract
Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.
- Published
- 2014
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15. Statistical physics approach to quantifying differences in myelinated nerve fibers.
- Author
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Comin CH, Santos JR, Corradini D, Morrison W, Curme C, Rosene DL, Gabrielli A, Costa Lda F, and Stanley HE
- Subjects
- Age Factors, Animals, Axons physiology, Axons ultrastructure, Cluster Analysis, Haplorhini, Nerve Fibers, Myelinated classification, Nerve Fibers, Myelinated ultrastructure
- Abstract
We present a new method to quantify differences in myelinated nerve fibers. These differences range from morphologic characteristics of individual fibers to differences in macroscopic properties of collections of fibers. Our method uses statistical physics tools to improve on traditional measures, such as fiber size and packing density. As a case study, we analyze cross-sectional electron micrographs from the fornix of young and old rhesus monkeys using a semi-automatic detection algorithm to identify and characterize myelinated axons. We then apply a feature selection approach to identify the features that best distinguish between the young and old age groups, achieving a maximum accuracy of 94% when assigning samples to their age groups. This analysis shows that the best discrimination is obtained using the combination of two features: the fraction of occupied axon area and the effective local density. The latter is a modified calculation of axon density, which reflects how closely axons are packed. Our feature analysis approach can be applied to characterize differences that result from biological processes such as aging, damage from trauma or disease or developmental differences, as well as differences between anatomical regions such as the fornix and the cingulum bundle or corpus callosum.
- Published
- 2014
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16. Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system.
- Author
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Cristino AS, Williams SM, Hawi Z, An JY, Bellgrove MA, Schwartz CE, Costa Lda F, and Claudianos C
- Subjects
- Databases, Genetic, Gene Expression Regulation genetics, Genetic Association Studies statistics & numerical data, Humans, MicroRNAs genetics, Transcription Factors genetics, Attention Deficit Disorder with Hyperactivity genetics, Child Development Disorders, Pervasive genetics, Genetic Predisposition to Disease genetics, Mental Retardation, X-Linked genetics, Models, Genetic, Schizophrenia genetics
- Abstract
Many putative genetic factors that confer risk to neurodevelopmental disorders such as autism spectrum disorders (ASDs) and X-linked intellectual disability (XLID), and to neuropsychiatric disorders including attention deficit hyperactivity disorder (ADHD) and schizophrenia (SZ) have been identified in individuals from diverse human populations. Although there is significant aetiological heterogeneity within and between these conditions, recent data show that genetic factors contribute to their comorbidity. Many studies have identified candidate gene associations for these mental health disorders, albeit this is often done in a piecemeal fashion with little regard to the inherent molecular complexity. Here, we sought to abstract relationships from our knowledge of systems level biology to help understand the unique and common genetic drivers of these conditions. We undertook a global and systematic approach to build and integrate available data in gene networks associated with ASDs, XLID, ADHD and SZ. Complex network concepts and computational methods were used to investigate whether candidate genes associated with these conditions were related through mechanisms of gene regulation, functional protein-protein interactions, transcription factor (TF) and microRNA (miRNA) binding sites. Although our analyses show that genetic variations associated with the four disorders can occur in the same molecular pathways and functional domains, including synaptic transmission, there are patterns of variation that define significant differences between disorders. Of particular interest is DNA variations located in intergenic regions that comprise regulatory sites for TFs or miRNA. Our approach provides a hypothetical framework, which will help discovery and analysis of candidate genes associated with neurodevelopmental and neuropsychiatric disorders.
- Published
- 2014
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17. Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy.
- Author
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Bando SY, Silva FN, Costa Lda F, Silva AV, Pimentel-Silva LR, Castro LH, Wen HT, Amaro E Jr, and Moreira-Filho CA
- Subjects
- CA3 Region, Hippocampal pathology, CA3 Region, Hippocampal physiopathology, Epilepsy, Temporal Lobe pathology, Epilepsy, Temporal Lobe physiopathology, Gene Expression Profiling, Humans, RNA, Messenger genetics, RNA, Messenger metabolism, Transcription, Genetic, CA3 Region, Hippocampal metabolism, Epilepsy, Temporal Lobe metabolism, Transcriptome
- Abstract
We previously described - studying transcriptional signatures of hippocampal CA3 explants - that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks.
- Published
- 2013
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18. Probing the statistical properties of unknown texts: application to the Voynich Manuscript.
- Author
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Amancio DR, Altmann EG, Rybski D, Oliveira ON Jr, and Costa Lda F
- Subjects
- Algorithms, Humans, Reading, Language, Models, Statistical, Semantics
- Abstract
While the use of statistical physics methods to analyze large corpora has been useful to unveil many patterns in texts, no comprehensive investigation has been performed on the interdependence between syntactic and semantic factors. In this study we propose a framework for determining whether a text (e.g., written in an unknown alphabet) is compatible with a natural language and to which language it could belong. The approach is based on three types of statistical measurements, i.e. obtained from first-order statistics of word properties in a text, from the topology of complex networks representing texts, and from intermittency concepts where text is treated as a time series. Comparative experiments were performed with the New Testament in 15 different languages and with distinct books in English and Portuguese in order to quantify the dependency of the different measurements on the language and on the story being told in the book. The metrics found to be informative in distinguishing real texts from their shuffled versions include assortativity, degree and selectivity of words. As an illustration, we analyze an undeciphered medieval manuscript known as the Voynich Manuscript. We show that it is mostly compatible with natural languages and incompatible with random texts. We also obtain candidates for keywords of the Voynich Manuscript which could be helpful in the effort of deciphering it. Because we were able to identify statistical measurements that are more dependent on the syntax than on the semantics, the framework may also serve for text analysis in language-dependent applications.
- Published
- 2013
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19. A methodology to infer gene networks from spatial patterns of expression--an application to fluorescence in situ hybridization images.
- Author
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Campiteli MG, Comin CH, Costa Lda F, Babu MM, and Cesar RM
- Subjects
- Animals, Drosophila embryology, Drosophila genetics, Gene Expression Regulation, Developmental, Gene Expression Profiling methods, Gene Regulatory Networks, Image Processing, Computer-Assisted methods, In Situ Hybridization, Fluorescence
- Abstract
The proper functional development of a multicellular organism depends on an intricate network of interacting genes that are expressed in accurate temporal and spatial patterns across different tissues. Complex inhibitory and excitatory interactions among genes control the territorial differences that explain specialized cell fates, embryo polarization and tissues architecture in metazoans. Given the nature of the regulatory gene networks, similarity of expression patterns can identify genes with similar roles. The inference and analysis of the gene interaction networks through complex network tools can reveal important aspects of the biological system modeled. Here we suggest an image analysis pipeline to quantify co-localization patterns in in situ hybridization images of Drosophila embryos and, based on these patterns, infer gene networks. We analyze the spatial dispersion of the gene expression and show the gene interaction networks for different developmental stages. Our results suggest that the inference of developmental networks based on spatial expression data is biologically relevant and represents a potential tool for the understanding of animal development.
- Published
- 2013
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20. Mitochondrial network size scaling in budding yeast.
- Author
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Rafelski SM, Viana MP, Zhang Y, Chan YH, Thorn KS, Yam P, Fung JC, Li H, Costa Lda F, and Marshall WF
- Subjects
- G1 Phase, Microscopy, Confocal, Saccharomyces cerevisiae cytology, Saccharomyces cerevisiae Proteins genetics, Saccharomyces cerevisiae Proteins metabolism, rab GTP-Binding Proteins genetics, rab GTP-Binding Proteins metabolism, Mitochondria metabolism, Mitochondria ultrastructure, Mitochondrial Size, Saccharomyces cerevisiae growth & development, Saccharomyces cerevisiae ultrastructure
- Abstract
Mitochondria must grow with the growing cell to ensure proper cellular physiology and inheritance upon division. We measured the physical size of mitochondrial networks in budding yeast and found that mitochondrial network size increased with increasing cell size and that this scaling relation occurred primarily in the bud. The mitochondria-to-cell size ratio continually decreased in aging mothers over successive generations. However, regardless of the mother's age or mitochondrial content, all buds attained the same average ratio. Thus, yeast populations achieve a stable scaling relation between mitochondrial content and cell size despite asymmetry in inheritance.
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- 2012
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21. Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.
- Author
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Antiqueira L, Janga SC, and Costa Lda F
- Subjects
- Escherichia coli genetics, Escherichia coli Proteins genetics, Gene Expression Regulation, Bacterial genetics, Gene Expression Regulation, Bacterial physiology, Gene Regulatory Networks, Principal Component Analysis, Transcription Factors genetics, Escherichia coli metabolism, Escherichia coli Proteins metabolism, Transcription Factors metabolism
- Abstract
To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.
- Published
- 2012
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22. Morphological homogeneity of neurons: searching for outlier neuronal cells.
- Author
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Zawadzki K, Feenders C, Viana MP, Kaiser M, and Costa Lda F
- Subjects
- Algorithms, Animals, Databases, Factual statistics & numerical data, Humans, Neurons physiology, Principal Component Analysis, Software, Models, Neurological, Neurons classification, Neurons cytology
- Abstract
We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the NeuroMorpho.org database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.
- Published
- 2012
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23. Prominent effect of soil network heterogeneity on microbial invasion.
- Author
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Pérez-Reche FJ, Taraskin SN, Otten W, Viana MP, Costa Lda F, and Gilligan CA
- Subjects
- Models, Biological, Soil chemistry, Soil Microbiology
- Abstract
Using a network representation for real soil samples and mathematical models for microbial spread, we show that the structural heterogeneity of the soil habitat may have a very significant influence on the size of microbial invasions of the soil pore space. In particular, neglecting the soil structural heterogeneity may lead to a substantial underestimation of microbial invasion. Such effects are explained in terms of a crucial interplay between heterogeneity in microbial spread and heterogeneity in the topology of soil networks. The main influence of network topology on invasion is linked to the existence of long channels in soil networks that may act as bridges for transmission of microorganisms between distant parts of soil.
- Published
- 2012
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- View/download PDF
24. Effective number of accessed nodes in complex networks.
- Author
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Viana MP, Batista JL, and Costa Lda F
- Abstract
The measurement called accessibility has been proposed as a means to quantify the efficiency of the communication between nodes in complex networks. This article reports results regarding the properties of accessibility, including its relationship with the average minimal time to visit all nodes reachable after h steps along a random walk starting from a source, as well as the number of nodes that are visited after a finite period of time. We characterize the relationship between accessibility and the average number of walks required in order to visit all reachable nodes (the exploration time), conjecture that the maximum accessibility implies the minimal exploration time, and confirm the relationship between the accessibility values and the number of nodes visited after a basic time unit. The latter relationship is investigated with respect to three types of dynamics: traditional random walks, self-avoiding random walks, and preferential random walks.
- Published
- 2012
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- View/download PDF
25. The structure and resilience of financial market networks.
- Author
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Peron TK, Costa Lda F, and Rodrigues FA
- Subjects
- Computer Simulation, Financial Management statistics & numerical data, Game Theory, Models, Economic, Nonlinear Dynamics, Social Support
- Abstract
Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.
- Published
- 2012
- Full Text
- View/download PDF
26. Study of cerebral gene expression densities using Voronoi analysis.
- Author
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Miazaki M and Costa Lda F
- Subjects
- Animals, High-Throughput Screening Assays, Mice, Principal Component Analysis, Cerebral Cortex physiology, Gene Expression Profiling methods
- Abstract
As the available public cerebral gene expression image data increasingly grows, the demand for automated methods to analyze such large amount of data also increases. An important study that can be carried out on these data is related to the spatial relationship between gene expressions. Similar spatial density distribution of expression between genes may indicate they are functionally correlated, thus the identification of these similarities is useful in suggesting directions of investigation to discover gene interactions and their correlated functions. In this paper, we describe the use of a high-throughput methodology based on Voronoi diagrams to automatically analyze and search for possible local spatial density relationships between gene expression images. We tested this method using mouse brain section images from the Allen Mouse Brain Atlas public database. This methodology provided measurements able to characterize the similarity of the density distribution between gene expressions and allowed the visualization of the results through networks and Principal Component Analysis (PCA). These visualizations are useful to analyze the similarity level between gene expression patterns, as well as to compare connection patterns between region networks. Some genes were found to have the same type of function and to be near each other in the PCA visualizations. These results suggest cerebral density correlations between gene expressions that could be further explored., (Copyright © 2011 Elsevier B.V. All rights reserved.)
- Published
- 2012
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- View/download PDF
27. Epithelial organisation revealed by a network of cellular contacts.
- Author
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Escudero LM, Costa Lda F, Kicheva A, Briscoe J, Freeman M, and Babu MM
- Subjects
- Animals, Cell Communication physiology, Drosophila, Epithelial Cells metabolism, Microscopy, Confocal, Models, Biological, Epithelial Cells cytology, Epithelium metabolism
- Abstract
The emergence of differences in the arrangement of cells is the first step towards the establishment of many organs. Understanding this process is limited by the lack of systematic characterization of epithelial organisation. Here we apply network theory at the scale of individual cells to uncover patterns in cell-to-cell contacts that govern epithelial organisation. We provide an objective characterisation of epithelia using network representation, where cells are nodes and cell contacts are links. The features of individual cells, together with attributes of the cellular network, produce a defining signature that distinguishes epithelia from different organs, species, developmental stages and genetic conditions. The approach permits characterization, quantification and classification of normal and perturbed epithelia, and establishes a framework for understanding molecular mechanisms that underpin the architecture of complex tissues.
- Published
- 2011
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28. Identifying the starting point of a spreading process in complex networks.
- Author
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Comin CH and Costa Lda F
- Subjects
- Algorithms, Animals, Computer Communication Networks, Computer Simulation, Humans, Models, Statistical, Neurons physiology, Probability, Social Support, Disease Transmission, Infectious, Epidemics
- Abstract
When dealing with the dissemination of epidemics, one important question that can be asked is the location where the contamination began. In this paper, we analyze three spreading schemes and propose and validate an effective methodology for the identification of the source nodes. The method is based on the calculation of the centrality of the nodes on the sampled network, expressed here by degree, betweenness, closeness, and eigenvector centrality. We show that the source node tends to have the highest measurement values. The potential of the methodology is illustrated with respect to three theoretical complex network models as well as a real-world network, the email network of the University Rovira i Virgili.
- Published
- 2011
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- View/download PDF
29. Gene expression complex networks: synthesis, identification, and analysis.
- Author
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Lopes FM, Cesar RM, and Costa Lda F
- Subjects
- Algorithms, Artificial Intelligence, Computer Simulation, Gene Expression, Synthetic Biology, Time Factors, Computational Biology methods, Gene Regulatory Networks genetics, Models, Genetic, Software Validation, Systems Biology methods
- Abstract
Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdös-Rényi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabási-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree
variation, decreasing its network recovery rate with the increase of . The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods. - Published
- 2011
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30. Resilience of protein-protein interaction networks as determined by their large-scale topological features.
- Author
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Rodrigues FA, Costa Lda F, and Barbieri AL
- Subjects
- Algorithms, Animals, Caenorhabditis elegans chemistry, Computational Biology, Databases, Protein, Drosophila melanogaster chemistry, Humans, Protein Binding, Saccharomyces cerevisiae Proteins chemistry, Proteins chemistry, Proteins metabolism
- Abstract
The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.
- Published
- 2011
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31. Epidemics in networks of spatially correlated three-dimensional root-branching structures.
- Author
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Handford TP, Pérez-Reche FJ, Taraskin SN, Costa Lda F, Miazaki M, Neri FM, and Gilligan CA
- Subjects
- Animals, Humans, Plants, Epidemics, Epidemiologic Methods, Epidemiology, Models, Biological
- Abstract
Using digitized images of the three-dimensional, branching structures for root systems of bean seedlings, together with analytical and numerical methods that map a common susceptible-infected-recovered ('SIR') epidemiological model onto the bond percolation problem, we show how the spatially correlated branching structures of plant roots affect transmission efficiencies, and hence the invasion criterion, for a soil-borne pathogen as it spreads through ensembles of morphologically complex hosts. We conclude that the inherent heterogeneities in transmissibilities arising from correlations in the degrees of overlap between neighbouring plants render a population of root systems less susceptible to epidemic invasion than a corresponding homogeneous system. Several components of morphological complexity are analysed that contribute to disorder and heterogeneities in the transmissibility of infection. Anisotropy in root shape is shown to increase resilience to epidemic invasion, while increasing the degree of branching enhances the spread of epidemics in the population of roots. Some extension of the methods for other epidemiological systems are discussed.
- Published
- 2011
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- View/download PDF
32. Automatic network fingerprinting through single-node motifs.
- Author
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Echtermeyer C, Costa Lda F, Rodrigues FA, and Kaiser M
- Subjects
- High-Throughput Screening Assays, Methods, Algorithms, Models, Biological, Models, Theoretical
- Abstract
Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.
- Published
- 2011
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- View/download PDF
33. Unveiling the neuromorphological space.
- Author
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Costa Lda F, Zawadzki K, Miazaki M, Viana MP, and Taraskin SN
- Abstract
This article proposes the concept of neuromorphological space as the multidimensional space defined by a set of measurements of the morphology of a representative set of almost 6000 biological neurons available from the NeuroMorpho database. For the first time, we analyze such a large database in order to find the general distribution of the geometrical features. We resort to McGhee's biological shape space concept in order to formalize our analysis, allowing for comparison between the geometrically possible tree-like shapes, obtained by using a simple reference model, and real neuronal shapes. Two optimal types of projections, namely, principal component analysis and canonical analysis, are used in order to visualize the originally 20-D neuron distribution into 2-D morphological spaces. These projections allow the most important features to be identified. A data density analysis is also performed in the original 20-D feature space in order to corroborate the clustering structure. Several interesting results are reported, including the fact that real neurons occupy only a small region within the geometrically possible space and that two principal variables are enough to account for about half of the overall data variability. Most of the measurements have been found to be important in representing the morphological variability of the real neurons.
- Published
- 2010
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- View/download PDF
34. Estimating complex cortical networks via surface recordings- a critical note.
- Author
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Antiqueira L, Rodrigues FA, van Wijk BC, Costa Lda F, and Daffertshofer A
- Subjects
- Algorithms, Brain anatomy & histology, Brain cytology, Cerebral Cortex cytology, Computer Simulation, Humans, Linear Models, Magnetoencephalography, Models, Neurological, Nerve Net cytology, Neurons physiology, Cerebral Cortex physiology, Electroencephalography, Nerve Net physiology
- Abstract
We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites. Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers. A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites. We used various complex network models for reference and tried to classify sampled versions of the "brain-like" network as one of these archetypes. It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes. For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network., (Copyright 2010 Elsevier Inc. All rights reserved.)
- Published
- 2010
- Full Text
- View/download PDF
35. Complexity and anisotropy in host morphology make populations less susceptible to epidemic outbreaks.
- Author
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Pérez-Reche FJ, Taraskin SN, Costa Lda F, Neri FM, and Gilligan CA
- Subjects
- Anisotropy, Disease Susceptibility, Ecosystem, Crops, Agricultural genetics, Disease Outbreaks
- Abstract
One of the challenges in epidemiology is to account for the complex morphological structure of hosts such as plant roots, crop fields, farms, cells, animal habitats and social networks, when the transmission of infection occurs between contiguous hosts. Morphological complexity brings an inherent heterogeneity in populations and affects the dynamics of pathogen spread in such systems. We have analysed the influence of realistically complex host morphology on the threshold for invasion and epidemic outbreak in an SIR (susceptible-infected-recovered) epidemiological model. We show that disorder expressed in the host morphology and anisotropy reduces the probability of epidemic outbreak and thus makes the system more resistant to epidemic outbreaks. We obtain general analytical estimates for minimally safe bounds for an invasion threshold and then illustrate their validity by considering an example of host data for branching hosts (salamander retinal ganglion cells). Several spatial arrangements of hosts with different degrees of heterogeneity have been considered in order to separately analyse the role of shape complexity and anisotropy in the host population. The estimates for invasion threshold are linked to morphological characteristics of the hosts that can be used for determining the threshold for invasion in practical applications.
- Published
- 2010
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- View/download PDF
36. Knowledge acquisition by networks of interacting agents in the presence of observation errors.
- Author
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Batista JB and Costa Lda F
- Abstract
In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Rényi or Barabási-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.
- Published
- 2010
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- View/download PDF
37. A graph-theoretical approach in brain functional networks. Possible implications in EEG studies.
- Author
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Fallani Fde V, Costa Lda F, Rodriguez FA, Astolfi L, Vecchiato G, Toppi J, Borghini G, Cincotti F, Mattia D, Salinari S, Isabella R, and Babiloni F
- 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 out fl ow 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" Omega 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.
- Published
- 2010
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38. Mechanosensitivity of astrocytes on optimized polyacrylamide gels analyzed by quantitative morphometry.
- Author
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Moshayedi P, Costa Lda F, Christ A, Lacour SP, Fawcett J, Guck J, and Franze K
- Subjects
- Acrylic Resins, Animals, Cells, Cultured, Computer Simulation, Gels, Humans, Rats, Stress, Mechanical, Astrocytes cytology, Astrocytes physiology, Cell Adhesion physiology, Focal Adhesions physiology, Mechanotransduction, Cellular physiology, Models, Biological, Shear Strength physiology
- Abstract
Cells are able to detect and respond to mechanical cues from their environment. Previous studies have investigated this mechanosensitivity on various cell types, including neural cells such as astrocytes. In this study, we have carefully optimized polyacrylamide gels, commonly used as compliant growth substrates, considering their homogeneity in surface topography, mechanical properties, and coating density, and identified several potential pitfalls for the purpose of mechanosensitivity studies. The resulting astrocyte response to growth on substrates with shear storage moduli of G' = 100 Pa and G' = 10 kPa was then evaluated as a function of coating density of poly-D-lysine using quantitative morphometric analysis. Astrocytes cultured on stiff substrates showed significantly increased perimeter, area, diameter, elongation, number of extremities and overall complexity if compared to those cultured on compliant substrates. A statistically significant difference in the overall morphological score was confirmed with an artificial intelligence-based shape analysis. The dependence of the cells' morphology on PDL coating density seemed to be weak compared to the effect of the substrate stiffness and was slightly biphasic, with a maximum at 10-100 µg ml(-1) PDL concentration. Our finding suggests that the compliance of the surrounding tissue in vivo may influence astrocyte morphology and behavior.
- Published
- 2010
- Full Text
- View/download PDF
39. Regulation of radial glial motility by visual experience.
- Author
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Tremblay M, Fugère V, Tsui J, Schohl A, Tavakoli A, Travençolo BA, Costa Lda F, and Ruthazer ES
- Subjects
- Animals, Calcium metabolism, In Vitro Techniques, Neurons physiology, Neuropil physiology, Nitric Oxide metabolism, Photic Stimulation, Pseudopodia physiology, Receptors, N-Methyl-D-Aspartate metabolism, Retinal Ganglion Cells physiology, Signal Transduction, Superior Colliculi growth & development, Synapses physiology, Visual Pathways growth & development, Visual Pathways physiology, Xenopus laevis, Cell Movement physiology, Neuroglia physiology, Superior Colliculi physiology, Visual Perception physiology
- Abstract
Radial glia in the developing optic tectum express the key guidance molecules responsible for topographic targeting of retinal axons. However, the extent to which the radial glia are themselves influenced by retinal inputs and visual experience remains unknown. Using multiphoton live imaging of radial glia in the optic tectum of intact Xenopus laevis tadpoles in conjunction with manipulations of neural activity and sensory stimuli, radial glia were observed to exhibit spontaneous calcium transients that were modulated by visual stimulation. Structurally, radial glia extended and retracted many filopodial processes within the tectal neuropil over minutes. These processes interacted with retinotectal synapses and their motility was modulated by nitric oxide (NO) signaling downstream of neuronal NMDA receptor (NMDAR) activation and visual stimulation. These findings provide the first in vivo demonstration that radial glia actively respond both structurally and functionally to neural activity, via NMDAR-dependent NO release during the period of retinal axon ingrowth.
- Published
- 2009
- Full Text
- View/download PDF
40. Studies of aberrant phyllotaxy1 mutants of maize indicate complex interactions between auxin and cytokinin signaling in the shoot apical meristem.
- Author
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Lee BH, Johnston R, Yang Y, Gallavotti A, Kojima M, Travençolo BA, Costa Lda F, Sakakibara H, and Jackson D
- Subjects
- Arabidopsis genetics, Arabidopsis metabolism, Cytokinins pharmacology, Gene Expression drug effects, Gene Expression Regulation, Plant, Indoleacetic Acids pharmacology, Luminescent Proteins analysis, Membrane Transport Proteins genetics, Membrane Transport Proteins metabolism, Mutation, Phthalimides pharmacology, Plant Growth Regulators pharmacology, Plant Shoots anatomy & histology, Plant Shoots drug effects, Plant Shoots genetics, Plant Shoots metabolism, Recombinant Fusion Proteins analysis, Seeds genetics, Seeds metabolism, Zea mays anatomy & histology, Zea mays drug effects, Zea mays genetics, Cytokinins metabolism, Indoleacetic Acids metabolism, Meristem metabolism, Plant Proteins genetics, Signal Transduction physiology, Zea mays metabolism
- Abstract
One of the most fascinating aspects of plant morphology is the regular geometric arrangement of leaves and flowers, called phyllotaxy. The shoot apical meristem (SAM) determines these patterns, which vary depending on species and developmental stage. Auxin acts as an instructive signal in leaf initiation, and its transport has been implicated in phyllotaxy regulation in Arabidopsis (Arabidopsis thaliana). Altered phyllotactic patterns are observed in a maize (Zea mays) mutant, aberrant phyllotaxy1 (abph1, also known as abphyl1), and ABPH1 encodes a cytokinin-inducible type A response regulator, suggesting that cytokinin signals are also involved in the mechanism by which phyllotactic patterns are established. Therefore, we investigated the interaction between auxin and cytokinin signaling in phyllotaxy. Treatment of maize shoots with a polar auxin transport inhibitor, 1-naphthylphthalamic acid, strongly reduced ABPH1 expression, suggesting that auxin or its polar transport is required for ABPH1 expression. Immunolocalization of the PINFORMED1 (PIN1) polar auxin transporter revealed that PIN1 expression marks leaf primordia in maize, similarly to Arabidopsis. Interestingly, maize PIN1 expression at the incipient leaf primordium was greatly reduced in abph1 mutants. Consistently, auxin levels were reduced in abph1, and the maize PIN1 homolog was induced not only by auxin but also by cytokinin treatments. Our results indicate distinct roles for ABPH1 as a negative regulator of SAM size and a positive regulator of PIN1 expression. These studies highlight a complex interaction between auxin and cytokinin signaling in the specification of phyllotactic patterns and suggest an alternative model for the generation of altered phyllotactic patterns in abph1 mutants. We propose that reduced auxin levels and PIN1 expression in abph1 mutant SAMs delay leaf initiation, contributing to the enlarged SAM and altered phyllotaxy of these mutants.
- Published
- 2009
- Full Text
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41. Protein lethality investigated in terms of long range dynamical interactions.
- Author
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Rodrigues FA and Costa Lda F
- Subjects
- Computer Simulation, Models, Biological, Protein Interaction Mapping, Saccharomyces cerevisiae metabolism, Systems Biology, Proteins metabolism
- Abstract
The relationship between network structure/dynamics and biological function constitutes a fundamental issue in systems biology. However, despite many related investigations, the correspondence between structure and biological functions is not yet fully understood. A related subject that has deserved particular attention recently concerns how essentiality is related to the structure and dynamics of protein interactions. In the current work, protein essentiality is investigated in terms of long range influences in protein-protein interaction networks by considering simulated dynamical aspects. This analysis is performed with respect to outward activations, an approach which models the propagation of interactions between proteins by considering self-avoiding random walks. The obtained results are compared to protein local connectivity. Both the connectivity and the outward activations were found to be strongly related to protein essentiality.
- Published
- 2009
- Full Text
- View/download PDF
42. Automatic contour extraction from 2D neuron images.
- Author
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Leandro JJ, Cesar RM Jr, and Costa Lda F
- Subjects
- Artifacts, Automation methods, Brain cytology, Brain physiology, Cell Shape, Dendrites physiology, Dendrites ultrastructure, Neurons classification, Neurons cytology, Neurons physiology, Normal Distribution, Retinal Ganglion Cells classification, Retinal Ganglion Cells physiology, Software, Software Validation, Algorithms, Image Processing, Computer-Assisted methods, Microscopy methods, Neuroanatomy methods, Pattern Recognition, Automated methods, Retinal Ganglion Cells cytology
- Abstract
This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods cannot be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, up to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology.
- Published
- 2009
- Full Text
- View/download PDF
43. Modularity and robustness of bone networks.
- Author
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Viana MP, Tanck E, Beletti ME, and Costa Lda F
- Subjects
- Animals, Chickens, Principal Component Analysis, Bone and Bones physiology
- Abstract
Cortical bones, essential for mechanical support and structure in many animals, involve a large number of canals organized in intricate fashion. By using state-of-the art image analysis and computer graphics, the 3D reconstruction of a whole bone (phalange) of a young chicken was obtained and represented in terms of a complex network where each canal was associated to an edge and every confluence of three or more canals yielded a respective node. The representation of the bone canal structure as a complex network has allowed several methods to be applied in order to characterize and analyze the canal system organization and the robustness. First, the distribution of the node degrees (i.e. the number of canals connected to each node) confirmed previous indications that bone canal networks follow a power law, and therefore present some highly connected nodes (hubs). The bone network was also found to be partitioned into communities or modules, i.e. groups of nodes which are more intensely connected to one another than with the rest of the network. We verified that each community exhibited distinct topological properties that are possibly linked with their specific function. In order to better understand the organization of the bone network, its resilience to two types of failures (random attack and cascaded failures) was also quantified comparatively to randomized and regular counterparts. The results indicate that the modular structure improves the robustness of the bone network when compared to a regular network with the same average degree and number of nodes. The effects of disease processes (e.g., osteoporosis) and mutations in genes (e.g., BMP4) that occur at the molecular level can now be investigated at the mesoscopic level by using network based approaches.
- Published
- 2009
- Full Text
- View/download PDF
44. Objective characterization of the course of the parasellar internal carotid artery using mathematical tools.
- Author
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Meng S, Geyer SH, Costa Lda F, Viana MP, and Weninger WJ
- Subjects
- Adult, Aged, Carotid Artery, Internal diagnostic imaging, Contrast Media administration & dosage, Female, Humans, Male, Middle Aged, Radiographic Image Enhancement methods, Young Adult, Algorithms, Carotid Artery, Internal anatomy & histology, Imaging, Three-Dimensional methods, Models, Cardiovascular, Tomography, X-Ray Computed methods
- Abstract
Background: Along the internal carotid artery (ICA), atherosclerotic plaques are often located in its cavernous sinus (parasellar) segments (pICA). Studies indicate that the incidence of pre-atherosclerotic lesions is linked with the complexity of the pICA; however, the pICA shape was never objectively characterized. Our study aims at providing objective mathematical characterizations of the pICA shape., Methods and Results: Three-dimensional (3D) computer models, reconstructed from contrast enhanced computed tomography (CT) data of 30 randomly selected patients (60 pICAs) were analyzed with modern visualization software and new mathematical algorithms. As objective measures for the pICA shape complexity, we provide calculations of curvature energy, torsion energy, and total complexity of 3D skeletons of the pICA lumen. We further measured the posterior knee of the so-called "carotid siphon" with a virtual goniometer and performed correlations between the objective mathematical calculations and the subjective angle measurements., Conclusions: Firstly, our study provides mathematical characterizations of the pICA shape, which can serve as objective reference data for analyzing connections between pICA shape complexity and vascular diseases. Secondly, we provide an objective method for creating such data. Thirdly, we evaluate the usefulness of subjective goniometric measurements of the angle of the posterior knee of the carotid siphon.
- Published
- 2008
- Full Text
- View/download PDF
45. Three-dimensional description and mathematical characterization of the parasellar internal carotid artery in human infants.
- Author
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Meng S, Costa Lda F, Geyer SH, Viana MP, Reiter C, Müller GB, and Weninger WJ
- Subjects
- Cavernous Sinus, Female, Humans, Infant, Infant, Newborn, Male, Tunica Intima anatomy & histology, Algorithms, Carotid Artery, Internal anatomy & histology, Image Processing, Computer-Assisted, Imaging, Three-Dimensional
- Abstract
Inside the 'cavernous sinus' or 'parasellar region' the human internal carotid artery takes the shape of a siphon that is twisted and torqued in three dimensions and surrounded by a network of veins. The parasellar section of the internal carotid artery is of broad biological and medical interest, as its peculiar shape is associated with temperature regulation in the brain and correlated with the occurrence of vascular pathologies. The present study aims to provide anatomical descriptions and objective mathematical characterizations of the shape of the parasellar section of the internal carotid artery in human infants and its modifications during ontogeny. Three-dimensional (3D) computer models of the parasellar section of the internal carotid artery of infants were generated with a state-of-the-art 3D reconstruction method and analysed using both traditional morphometric methods and novel mathematical algorithms. We show that four constant, demarcated bends can be described along the infant parasellar section of the internal carotid artery, and we provide measurements of their angles. We further provide calculations of the curvature and torsion energy, and the total complexity of the 3D skeleton of the parasellar section of the internal carotid artery, and compare the complexity of this in infants and adults. Finally, we examine the relationship between shape parameters of the parasellar section of the internal carotid artery in infants, and the occurrence of intima cushions, and evaluate the reliability of subjective angle measurements for characterizing the complexity of the parasellar section of the internal carotid artery in infants. The results can serve as objective reference data for comparative studies and for medical imaging diagnostics. They also form the basis for a new hypothesis that explains the mechanisms responsible for the ontogenetic transformation in the shape of the parasellar section of the internal carotid artery.
- Published
- 2008
- Full Text
- View/download PDF
46. Chain motifs: the tails and handles of complex networks.
- Author
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Boas PR, Rodrigues FA, Travieso G, and Costa Lda F
- Abstract
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.
- Published
- 2008
- Full Text
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47. Predicting the connectivity of primate cortical networks from topological and spatial node properties.
- Author
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Costa Lda F, Kaiser M, and Hilgetag CC
- Subjects
- Animals, Behavior, Animal, Caenorhabditis elegans anatomy & histology, Caenorhabditis elegans physiology, Cerebral Cortex physiology, Macaca physiology, Nerve Net physiology, Cerebral Cortex anatomy & histology, Macaca anatomy & histology, Nerve Net anatomy & histology, Systems Biology methods
- 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.
- Published
- 2007
- Full Text
- View/download PDF
48. Voronoi analysis uncovers relationship between mosaics of normally placed and displaced amacrine cells in the thraira retina.
- Author
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Costa Lda F, Bonci DM, Saito CA, Rocha FA, Silveira LC, and Ventura DF
- Subjects
- Animals, Fishes, Visual Pathways anatomy & histology, Visual Pathways physiology, Amacrine Cells cytology, Cell Communication physiology, Mathematical Computing, Retina cytology, Software, Synapses physiology
- Abstract
Although neuronal dynamics is to a high extent a function of synapse strength, the spatial distribution of neurons is also known to play an important role, which is evidenced by the topographical organization of the main stations of the visual system: retina, lateral geniculate nucleus, and cortex. The coexisting systems of normally placed and displaced amacrine cells in the vertebrate retina provide interesting examples of retinotopic spatial organization. However, it is not clear whether these two systems are spatially interrelated or not. The current work applies two mathematical-computational methods-a new method involving Voronoi diagrams for local density quantification and a more traditional approach, the Ripley K function-in order to characterize the mosaics of normally placed and displaced amacrine cells in the retina of Hoplias malabaricus and search for possible spatial relationships between these two types of mosaics. The results obtained by the Voronoi local density analysis suggest that the two systems of amacrine cells are spatially interrelated through nearly constant local density ratios.
- Published
- 2007
- Full Text
- View/download PDF
49. A new method for quantifying three-dimensional interactions between biological structures.
- Author
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Travençolo BA, Martínez Debat C, Beletti ME, Sotelo Silveira JR, Ehrlich R, and Costa Lda F
- Subjects
- Animals, Cell Nucleus ultrastructure, Echinococcosis, Life Cycle Stages, Microscopy, Confocal, Echinococcus granulosus growth & development, Echinococcus granulosus ultrastructure, Image Processing, Computer-Assisted, Imaging, Three-Dimensional
- Abstract
In this paper we examine a new distance-based method for identifying and characterizing possible interactions between biological structures and objects, with respect to the initial developmental stages of Echinococcus granulosus. By adopting the surface of the foramen as the distance reference, several interesting results have been identified, including the fact that the cell nuclei tend to be organized with respect to the foramen surface as well as the stability of the spatial distribution of these nuclei along the development stages.
- Published
- 2007
- Full Text
- View/download PDF
50. Exploring complex networks through random walks.
- Author
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Costa Lda F and Travieso G
- Abstract
Most real complex networks--such as protein interactions, social contacts, and the Internet--are only partially known and available to us. While the process of exploring such networks in many cases resembles a random walk, it becomes a key issue to investigate and characterize how effectively the nodes and edges of such networks can be covered by different strategies. At the same time, it is critically important to infer how well can topological measurements such as the average node degree and average clustering coefficient be estimated during such network explorations. The present article addresses these problems by considering random, Barabási-Albert (BA), and geographical network models with varying connectivity explored by three types of random walks: traditional, preferential to untracked edges, and preferential to unvisited nodes. A series of relevant results are obtained, including the fact that networks of the three studied models with the same size and average node degree allow similar node and edge coverage efficiency, the identification of linear scaling with the size of the network of the random walk step at which a given percentage of the nodes/edges is covered, and the critical result that the estimation of the averaged node degree and clustering coefficient by random walks on BA networks often leads to heavily biased results. Many are the theoretical and practical implications of such results.
- Published
- 2007
- Full Text
- View/download PDF
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