Self-similar community structure in a network of human interactions

We propose a procedure for analyzing and characterizing complex networks. We apply this to the social network as constructed from email communications within a medium sized university with about 1700 employees. Email networks provide an accurate and nonintrusive description of the flow of information within human organizations. Our results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar. This suggests that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

Impact of community structure on information transfer

The observation that real complex networks have internal structure has important implication for dynamic processes occurring on such topologies. Here we investigate the impact of community structure on a model of information transfer able to deal with both search and congestion simultaneously. We show that networks with fuzzy community structure are more efficient in terms of packet delivery than those with pronounced community structure. We also propose an alternative packet routing algorithm which takes advantage of the knowledge of communities to improve information transfer and show that in the context of the model an intermediate level of community structure is optimal. Finally, we show that in a hierarchical network setting, providing knowledge of communities at the level of highest modularity will improve network capacity by the largest amount.

Topology of the world trade web

Economy, and consequently trade, is a fundamental part of human social organization which, until now, has not been studied within the network modeling framework. Here we present the first, to the best of our knowledge, empirical characterization of the world trade web, that is, the network built upon the trade relationships between different countries in the world. This network displays the typical properties of complex networks, namely, scale-free degree distribution, the small-world property, a high clustering coefficient, and, in addition, degree-degree correlation between different vertices. All these properties make the world trade web a complex network, which is far from being well described through a classical random network description.

Arbuscular mycorrhizal fungi mediate below-ground plant-herbivore interactions: a phylogenetic study

Ecological interactions are complex networks, but have typically been studied in a pairwise fashion. Examining how third-party species can modify the outcome of pairwise interactions may allow us to better predict their outcomes in realistic systems. For instance, arbuscular mycorrhizal fungi (AMF) can affect plant interactions with other organisms, including below-ground herbivores, but the mechanisms underlying these effects remain unclear. Here, we use a comparative, phylogenetically controlled approach to test the relative importance of mycorrhizal colonization and plant chemical defences (cardenolides) in predicting plant survival and the abundance of a generalist below-ground herbivore across 14 species of milkweeds (Asclepias spp.). Plants were inoculated with a mixture of four generalist AMF species or left uninoculated. After 1month, larvae of Bradysia sp. (Diptera: Sciaridae), a generalist below-ground herbivore, colonized plant roots. We performed phylogenetically controlled analyses to assess the influence of AMF colonization and toxic cardenolides on plant growth, mortality and infestation by fungus gnats. Overall, plants inoculated with AMF exhibited greater survival than did uninoculated plants. Additionally, surviving inoculated plants had lower numbers of larvae in their roots and fewer non-AM fungi than surviving uninoculated plants. In phylogenetic controlled regressions, gnat density in roots was better predicted by the extent of root colonized by AMF than by root cardenolide concentration. Taken as a whole, AMF modify the effect of below-ground herbivores on plants in a species-specific manner, independent of changes in chemical defence. This study adds to the growing body of literature demonstrating that mycorrhizal fungi may improve plant fitness by conferring protection against antagonists, rather than growth benefits. In addition, we advocate using comparative analyses to disentangle the roles of shared history and ecology in shaping trait expression and to better predict the outcomes of complex multitrophic interactions.

Self-similar community structure in a network of human interactions

We propose a procedure for analyzing and characterizing complex networks. We apply this to the social network as constructed from email communications within a medium sized university with about 1700 employees. Email networks provide an accurate and nonintrusive description of the flow of information within human organizations. Our results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar. This suggests that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

A high-resolution anatomical atlas of the transcriptome in the mouse embryo.

Ascertaining when and where genes are expressed is of crucial importance to understanding or predicting the physiological role of genes and proteins and how they interact to form the complex networks that underlie organ development and function. It is, therefore, crucial to determine on a genome-wide level, the spatio-temporal gene expression profiles at cellular resolution. This information is provided by colorimetric RNA in situ hybridization that can elucidate expression of genes in their native context and does so at cellular resolution. We generated what is to our knowledge the first genome-wide transcriptome atlas by RNA in situ hybridization of an entire mammalian organism, the developing mouse at embryonic day 14.5. This digital transcriptome atlas, the Eurexpress atlas (http://www.eurexpress.org), consists of a searchable database of annotated images that can be interactively viewed. We generated anatomy-based expression profiles for over 18,000 coding genes and over 400 microRNAs. We identified 1,002 tissue-specific genes that are a source of novel tissue-specific markers for 37 different anatomical structures. The quality and the resolution of the data revealed novel molecular domains for several developing structures, such as the telencephalon, a novel organization for the hypothalamus, and insight on the Wnt network involved in renal epithelial differentiation during kidney development. The digital transcriptome atlas is a powerful resource to determine co-expression of genes, to identify cell populations and lineages, and to identify functional associations between genes relevant to development and disease.

Modelització i simulació de processos dinàmics en xarxes complexes adaptatives

El 1736, Leonhard Euler va ser pioner en l'estudi de la teoria de grafs, i des de llavorsmúltiples autors com Kirchoff, Seymour, etc. continuaren amb l'estudi de la teoria i topologiade grafs. La teoria de xarxes, part de la teoria de grafs, també ha estat estudiada abastament.D'altra banda, la dinàmica de xarxes fou popularitzada per Dan Gillespie el 1977, en el qual proposà un algorisme que permet la simulació discreta i estocàstica d'un sistema de partícules, el qual és la base del treball ja que serveix per dur a terme les simulacions de processos sobre les xarxes complexes. El camp de l'anàlisi de la dinàmica de xarxes, de fet, és un campemergent en l'actualitat; comprèn tant l'anàlisi estadística com la utilització de simulacions persolucionar problemes de la mateixa dinàmica.Les xarxes complexes (xarxes de característiques complexes, sovint xarxes reals) també sónobjecte d'estudi de l'actualitat, sobretot a causa de l'aparició de les xarxes socials. S'han convertiten un paradigma per l'estudi de processos dinàmics en sistemes formats per molts componentsque interactuen entre si de manera molt homogèniaL'objectiu del treball és triple:1. Estudiar i entendre els conceptes bàsics i la topologia de les xarxes complexes, així comdiferents tipus de dinàmiques de processos sobre elles.2. Programar un simulador estocàstic en llenguatge C++ capaç de generar trajectòries mitjantçant l'algorisme de Gillespie tant pel model epidèmic com pel model de dinàmicad'enllaços amb reconnexió.3. Utilitzar el simulador tant per estudiar casos que ja han estat tractats en la literatura comcasos nous que no han estat tractats i que poden ser assimilables a xarxes reals com, perexemple, xarxes socials

UMTS-radioverkon ja GSM-runkoverkon yhdistävän moduulin järjestelmätestaus

Tämän diplomityön aiheena oli tutkia ATM-moduulin järjestelmätestaustarpeita. ATM-moduuli on osa Nokian kolmannen generaation matkapuhelinverkkoarkkitehtuuria. Työ tehtiin Nokia Networks Oyj:lle MSC&HLR-tuotelinjan järjestelmätestausjaokselle. Työssä käsitellään aluksi GSM- ja UMTS-verkkojen arkkitehtuureja, protokollia ja toimintoja, sekä tutustutaan ATM-tekniikkaan, jota käytetään UMTS-liityntäverkon tiedonvälityksessä. Tämän jälkeen perehdytään ATM-moduulin rakenteeseen ja toimintoihin. Työssä perehdytään järjestelmätestauksen eri osa-alueisiin, niiden testilaitteille asettamiin vaatimuksiin sekä hahmotellaan järjestelmätestauksen päälinjoja.

Emergence of assortative mixing between clusters of cultured neurons

The analysis of the activity of neuronal cultures is considered to be a good proxy of the functional connectivity of in vivo neuronal tissues. Thus, the functional complex network inferred from activity patterns is a promising way to unravel the interplay between structure and functionality of neuronal systems. Here, we monitor the spontaneous self-sustained dynamics in neuronal cultures formed by interconnected aggregates of neurons (clusters). Dynamics is characterized by the fast activation of groups of clusters in sequences termed bursts. The analysis of the time delays between clusters' activations within the bursts allows the reconstruction of the directed functional connectivity of the network. We propose a method to statistically infer this connectivity and analyze the resulting properties of the associated complex networks. Surprisingly enough, in contrast to what has been reported for many biological networks, the clustered neuronal cultures present assortative mixing connectivity values, meaning that there is a preference for clusters to link to other clusters that share similar functional connectivity, as well as a rich-club core, which shapes a"connectivity backbone" in the network. These results point out that the grouping of neurons and the assortative connectivity between clusters are intrinsic survival mechanisms of the culture.

A new concept in prophylaxis and therapy: paramunization by poxvirus inducers

The so-called primitive, innate or paraspecific immune system is the phylogenetically older part of the complex immune system. It enables the organism to immediately attack various foreign substances, infectious pathogens, toxins and transformed cells of the organism itself. ,,Paramunity" is defined as an optimal regulated and activated, antigen-nonspecific defence, acquired through continuous active and succesful confrontation with endogenous and exogenous noxes or by means of ,,paramunization" with so called ,,paramunity inducers". Paramunity inducers based on different pox virus species (e.g. Baypamun®, Duphapind®, Conpind) have turned out to be effective and safe when applied with human beings as well as with animals. Pox virus inducers activate phagocytosis and NK-cells in addition to regulation of various cytokines, notably interferon a and g, IL 1, 2, CSF and TNF which comprise the network of the complex paraspecific immune system. The results of experimental work as well as practical use in veterinary medicine have shown that paramunization by pox inducers goes far beyond the common understanding of so-called ,,immuno-therapy". They are ,,bioregulators", because they have 1. a regulatory effect on a disturbed immune system in the sense of an optimal homoeostasis, and 2. simultaneously a regulatory effect between the immune, nervous, circulatory and hormone system. Therefore, the use of paramunization by pox inducers opens a new way of prophylaxis and therapy, not only with regard to infections, but also with regard to different other indications.

Critical elements underpinning the emergence of the medical game ecosystem: gamifying traumatic brain injury rehabilitation in Finland

The healthcare sector is currently in the verge of a reform and thus, the medical game research provide an interesting area of research. The aim of this study is to explore the critical elements underpinning the emergence of the medical game ecosystem with three sub-objectives: (1) to seek who are the key actors involved in the medical game ecosystem and identify their needs, (2) to scrutinise what types of resources are required in medical game development and what types of relationships are needed to secure those resources, and (3) to identify the existing institutions (‘the rules of the game’) affecting the emergence of the medical game ecosystem. The theoretical background consists of service ecosystems literature. The empirical study conducted is based on the semi-structured theme interviews of 25 experts in three relevant fields: games and technology, health and funding. The data was analysed through a theoretical framework that was designed based upon service ecosystems literature. The study proposes that the key actors are divided into five groups: medical game companies, customers, funders, regulatory parties and complementors. Their needs are linked to improving patient motivation and enhancing the healthcare processes resulting in lower costs. Several types of resources, especially skills and knowledge, are required to create a medical game. To gain access to those resources, medical game companies need to build complex networks of relationships. Proficiency in managing those value networks is crucial. In addition, the company should take into account the underlying institutions in the healthcare sector affecting the medical game ecosystem. Three crucial institutions were identified: validation, lack of innovation supporting structures in healthcare and the rising consumerisation. Based on the findings, medical games cannot be made in isolation. A developmental trajectory model of the emerging medical game ecosystem was created based on the empirical data. The relevancy of relationships and resources is dependent on the trajectory that the medical game company at that time resides. Furthermore, creating an official and documented database for clinically valdated medical games was proposed to establish the medical game market and ensure an adequate status for the effective medical games. Finally, ecosystems approach provides interesting future opportunities for research on medical game ecosystems.

Information management in aircraft maintenance

Well managed information promotes competitive advantage and economic value for the company. The challenge is to use information effectively in complex networks. Decision making in network is complicated due to many independent sources of information. The aim of the present study was to identify and map the internal information flows and used information resourced by functions and roles, to make proposals to the case organization to improve the information management and to improve the situational awareness and process flows. In the present study, an inductive approach was applied, with the aim to find out gaps and bottlenecks of information flow of an aircraft maintenance organization and its network. The empirical part was conducted with observing the processes and with questionnaires. Theoretical part of this study consists on reviewing relevant literature on maintenance management in aviation and information management in aviation. Together with empirical evidence and the literature used in the study the gaps were found and suggestions for improvements were done. The outcome of this study contributes the organization in its bigger goal to improve the productivity. The information management of the network is one actor in the field and will pave the way to smoother operation and situational awareness. The lack of rules and requirements for information management and spreading is a challenge in information management. The excessive data overburden may cause problem in the actors’ situation-awareness due to non-availability of the right information.

Critical elements underpinning the emergence of the medical game ecosystem:gamifying traumatic brain injury rehabilitation in Finland

The healthcare sector is currently in the verge of a reform and thus, the medical game research provide an interesting area of research. The aim of this study is to explore the critical elements underpinning the emergence of the medical game ecosystem with three sub-objectives: (1) to seek who are the key actors involved in the medical game ecosystem and identify their needs, (2) to scrutinise what types of resources are required in medical game development and what types of relationships are needed to secure those resources, and (3) to identify the existing institutions (‘the rules of the game’) affecting the emergence of the medical game ecosystem. The theoretical background consists of service ecosystems literature. The empirical study conducted is based on the semi-structured theme interviews of 25 experts in three relevant fields: games and technology, health and funding. The data was analysed through a theoretical framework that was designed based upon service ecosystems literature. The study proposes that the key actors are divided into five groups: medical game companies, customers, funders, regulatory parties and complementors. Their needs are linked to improving patient motivation and enhancing the healthcare processes resulting in lower costs. Several types of resources, especially skills and knowledge, are required to create a medical game. To gain access to those resources, medical game companies need to build complex networks of relationships. Proficiency in managing those value networks is crucial. In addition, the company should take into account the underlying institutions in the healthcare sector affecting the medical game ecosystem. Three crucial institutions were identified: validation, lack of innovation supporting structures in healthcare and the rising consumerisation. Based on the findings, medical games cannot be made in isolation. A developmental trajectory model of the emerging medical game ecosystem was created based on the empirical data. The relevancy of relationships and resources is dependent on the trajectory that the medical game company at that time resides. Furthermore, creating an official and documented database for clinically validated medical games was proposed to establish the medical game market and ensure an adequate status for the effective medical games. Finally, ecosystems approach provides interesting future opportunities for research on medical game ecosystems

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.