Reconciliation of metabolites and biochemical reactions for metabolic networks.

Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.

Biana: a software framework for compiling biological interactions and analyzing networks

Background: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.Results: We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.Conclusions: BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Multi-scale integration and predictability in resting state brain activity.

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.

B2B Integration in Global Supply Chains: An Identification of Technical Integration Scenarios

The competitiveness of businesses is increasingly dependent on their electronic networks with customers, suppliers, and partners. While the strategic and operational impact of external integration and IOS adoption has been extensively studied, much less attention has been paid to the organizational and technical design of electronic relationships. The objective of our longitudinal research project is the development of a framework for understanding and explaining B2B integration. Drawing on existing literature and empirical cases we present a reference model (a classification scheme for B2B Integration). The reference model comprises technical, organizational, and institutional levels to reflect the multiple facets of B2B integration. In this paper we onvestigate the current state of electronic collaboration in global supply chains focussing on the technical view. Using an indepth case analysis we identify five integration scenarios. In the subsequent confirmatory phase of the research we analyse 112 real-world company cases to validate these five integration scenarios. Our research advances and deepens existing studies by developing a B2B reference model, which reflects the current state of practice and is independent of specific implementation technologies. In the next stage of the research the emerging reference model will be extended to create an assessment model for analysing the maturity level of a given company in a specific supply chain.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Interhemispheric visual integration in humans explored by multimodal brain imaging

Résumé: Les récents progrès techniques de l'imagerie cérébrale non invasives ont permis d'améliorer la compréhension des différents systèmes fonctionnels cérébraux. Les approches multimodales sont devenues indispensables en recherche, afin d'étudier dans sa globalité les différentes caractéristiques de l'activité neuronale qui sont à la base du fonctionnement cérébral. Dans cette étude combinée d'imagerie par résonance magnétique fonctionnelle (IRMf) et d'électroencéphalographie (EEG), nous avons exploité le potentiel de chacune d'elles, soit respectivement la résolution spatiale et temporelle élevée. Les processus cognitifs, de perception et de mouvement nécessitent le recrutement d'ensembles neuronaux. Dans la première partie de cette thèse nous étudions, grâce à la combinaison des techniques IRMf et EEG, la réponse des aires visuelles lors d'une stimulation qui demande le regroupement d'éléments cohérents appartenant aux deux hémi-champs visuels pour en faire une seule image. Nous utilisons une mesure de synchronisation (EEG de cohérence) comme quantification de l'intégration spatiale inter-hémisphérique et la réponse BOLD (Blood Oxygenation Level Dependent) pour évaluer l'activité cérébrale qui en résulte. L'augmentation de la cohérence de l'EEG dans la bande beta-gamma mesurée au niveau des électrodes occipitales et sa corrélation linéaire avec la réponse BOLD dans les aires de VP/V4, reflète et visualise un ensemble neuronal synchronisé qui est vraisemblablement impliqué dans le regroupement spatial visuel. Ces résultats nous ont permis d'étendre la recherche à l'étude de l'impact que le contenu en fréquence des stimuli a sur la synchronisation. Avec la même approche, nous avons donc identifié les réseaux qui montrent une sensibilité différente à l'intégration des caractéristiques globales ou détaillées des images. En particulier, les données montrent que l'implication des réseaux visuels ventral et dorsal est modulée par le contenu en fréquence des stimuli. Dans la deuxième partie nous avons a testé l'hypothèse que l'augmentation de l'activité cérébrale pendant le processus de regroupement inter-hémisphérique dépend de l'activité des axones calleux qui relient les aires visuelles. Comme le Corps Calleux présente une maturation progressive pendant les deux premières décennies, nous avons analysé le développement de la fonction d'intégration spatiale chez des enfants âgés de 7 à 13 ans et le rôle de la myelinisation des fibres calleuses dans la maturation de l'activité visuelle. Nous avons combiné l'IRMf et la technique de MTI (Magnetization Transfer Imaging) afin de suivre les signes de maturation cérébrale respectivement sous l'aspect fonctionnel et morphologique (myelinisation). Chez lés enfants, les activations associées au processus d'intégration entre les hémi-champs visuels sont, comme chez l'adulte, localisées dans le réseau ventral mais se limitent à une zone plus restreinte. La forte corrélation que le signal BOLD montre avec la myelinisation des fibres du splenium est le signe de la dépendance entre la maturation des fonctions visuelles de haut niveau et celle des connections cortico-corticales. Abstract: Recent advances in non-invasive brain imaging allow the visualization of the different aspects of complex brain dynamics. The approaches based on a combination of imaging techniques facilitate the investigation and the link of multiple aspects of information processing. They are getting a leading tool for understanding the neural basis of various brain functions. Perception, motion, and cognition involve the formation of cooperative neuronal assemblies distributed over the cerebral cortex. In this research, we explore the characteristics of interhemispheric assemblies in the visual brain by taking advantage of the complementary characteristics provided by EEG (electroencephalography) and fMRI (Functional Magnetic Resonance Imaging) techniques. These are the high temporal resolution for EEG and high spatial resolution for fMRI. In the first part of this thesis we investigate the response of the visual areas to the interhemispheric perceptual grouping task. We use EEG coherence as a measure of synchronization and BOLD (Blood Oxygenar tion Level Dependent) response as a measure of the related brain activation. The increase of the interhemispheric EEG coherence restricted to the occipital electrodes and to the EEG beta band and its linear relation to the BOLD responses in VP/V4 area points to a trans-hemispheric synchronous neuronal assembly involved in early perceptual grouping. This result encouraged us to explore the formation of synchronous trans-hemispheric networks induced by the stimuli of various spatial frequencies with this multimodal approach. We have found the involvement of ventral and medio-dorsal visual networks modulated by the spatial frequency content of the stimulus. Thus, based on the combination of EEG coherence and fMRI BOLD data, we have identified visual networks with different sensitivity to integrating low vs. high spatial frequencies. In the second part of this work we test the hypothesis that the increase of brain activity during perceptual grouping depends on the activity of callosal axons interconnecting the visual areas that are involved. To this end, in children of 7-13 years, we investigated functional (functional activation with fMRI) and morphological (myelination of the corpus callosum with Magnetization Transfer Imaging (MTI)) aspects of spatial integration. In children, the activation associated with the spatial integration across visual fields was localized in visual ventral stream and limited to a part of the area activated in adults. The strong correlation between individual BOLD responses in .this area and the myelination of the splenial system of fibers points to myelination as a significant factor in the development of the spatial integration ability.

Extrasynaptic neurotransmission in the modulation of brain function. Focus on the striatal neuronal glial networks

Extrasynaptic neurotransmission is an important short distance form of volume transmission (VT) and describes the extracellular diffusion of transmitters and modulators after synaptic spillover or extrasynaptic release in the local circuit regions binding to and activating mainly extrasynaptic neuronal and glial receptors in the neuroglial networks of the brain. Receptor-receptor interactions in G protein-coupled receptor (GPCR) heteromers play a major role, on dendritic spines and nerve terminals including glutamate synapses, in the integrative processes of the extrasynaptic signaling. Heteromeric complexes between GPCR and ion-channel receptors play a special role in the integration of the synaptic and extrasynaptic signals. Changes in extracellular concentrations of the classical synaptic neurotransmitters glutamate and GABA found with microdialysis is likely an expression of the activity of the neuron-astrocyte unit of the brain and can be used as an index of VT-mediated actions of these two neurotransmitters in the brain. Thus, the activity of neurons may be functionally linked to the activity of astrocytes, which may release glutamate and GABA to the extracellular space where extrasynaptic glutamate and GABA receptors do exist. Wiring transmission (WT) and VT are fundamental properties of all neurons of the CNS but the balance between WT and VT varies from one nerve cell population to the other. The focus is on the striatal cellular networks, and the WT and VT and their integration via receptor heteromers are described in the GABA projection neurons, the glutamate, dopamine, 5-hydroxytryptamine (5-HT) and histamine striatal afferents, the cholinergic interneurons, and different types of GABA interneurons. In addition, the role in these networks of VT signaling of the energy-dependent modulator adenosine and of endocannabinoids mainly formed in the striatal projection neurons will be underlined to understand the communication in the striatal cellular networks

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Systems Genetics of Metabolism: The Use of the BXD Murine Reference Panel for Multiscalar Integration of Traits.

Metabolic homeostasis is achieved by complex molecular and cellular networks that differ significantly among individuals and are difficult to model with genetically engineered lines of mice optimized to study single gene function. Here, we systematically acquired metabolic phenotypes by using the EUMODIC EMPReSS protocols across a large panel of isogenic but diverse strains of mice (BXD type) to study the genetic control of metabolism. We generated and analyzed 140 classical phenotypes and deposited these in an open-access web service for systems genetics (www.genenetwork.org). Heritability, influence of sex, and genetic modifiers of traits were examined singly and jointly by using quantitative-trait locus (QTL) and expression QTL-mapping methods. Traits and networks were linked to loci encompassing both known variants and novel candidate genes, including alkaline phosphatase (ALPL), here linked to hypophosphatasia. The assembled and curated phenotypes provide key resources and exemplars that can be used to dissect complex metabolic traits and disorders.

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.

Implicit methods for qualitative modeling of gene regulatory networks.

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

On an IDS Model for Mobile Ad Hoc Networks

Manet security has a lot of open issues. Due to its character-istics, this kind of network needs preventive and corrective protection. Inthis paper, we focus on corrective protection proposing an anomaly IDSmodel for Manet. The design and development of the IDS are consideredin our 3 main stages: normal behavior construction, anomaly detectionand model update. A parametrical mixture model is used for behav-ior modeling from reference data. The associated Bayesian classi¯cationleads to the detection algorithm. MIB variables are used to provide IDSneeded information. Experiments of DoS and scanner attacks validatingthe model are presented as well.

Is blood still thicker than water? A life-course perspective on the transformation of family and friends' roles in personal networks

This thesis addresses the issue of the moving boundaries between family and friends' roles in personal networks, adopting a life-course perspective and using Switzerland as a case study. In a period of major changes in personal life happening in contemporary Western societies, understanding the organization of personal networks intertwined with the unfolding of individual life courses is of prime importance in facing new challenges with regard to social integration. The data stem from a representative national survey carried out in 2011 named Family tiMes, including 803 individuals born either in 1950-1955 or in 1970-1975. An innovative research design was adopted, combing cross-sectional ego-centered network data and retrospective longitudinal life-course data. The results show continuing boundaries between family and friends' roles and that family keeps a prominent role in personal networks despite the notable importance of friendship ties. One relationship stands out above all, that with the partner, followed quite a few steps behind by those with children. Regarding life courses, de-standardization tendencies were found in family formation and also a persistent gendering of occupational trajectories. Two kinds of life trajectories are particularly intertwined with personal networks, co-residence and partnership trajectories, both related to the unfolding of family life. In particular, transition to parenthood functions as a turning point in individuals' lives, deeply transforming their sociability. Finally, a twofold pluralization process was identified, affecting simultaneously the organization of personal networks and the unfolding of individual life courses. This thesis contributes to the literature on the sociology of family and personal life, and to fruitful interlinkage between the network approach and the life-course perspective.

Using narratives to study the impact of social networks on the educational paths of immigrant students

This paper presents the qualitative data collection process aimed at the study of the impactsocial relations and networks have on educational paths of immigrant students. In theframework of a R & D longitudinal study funded by the Ministry of Science and Innovation(2012-2014), the research team tracked the path of 87 immigrant students, from whom only 17successfully achieved the transition through the first and second year of Post-16 Education.A vast range of literature notes that relationships are an important part of migration process andsocial integration analysis, as well as school history in terms of success or failure. Through thefieldwork researchers collect the personal networks of all immigrant students from 3 highschools who were at that time attending last course of compulsory school. The network structureinfluences their social capital and therefore determines the resources, goods and types of supportindividuals can access. All these aspects are influential elements in the configuration anddevelopment of academic trajectories of immigrant students.At the end of the second year of Post-16 Education (two years later), the study captures personalnetworks of these students again, analyses and discusses their evolution and influence on theirpaths through qualitative interviews. Such interviews facilitated the discussion of theirrelationships while providing interesting narratives that are presented in the text. In order to do so, the biographical interpretive narrative method of interviewing is implemented.