The relationship between competencies acquired through Swiss academic sports science courses and the job requirements

In view of the changes in and growing variety of sports-related occupations, it is highly relevant for educational institutions 10 to know how well the educational contents of their sport science courses meet the professional requirements. This study analyses the relationship between the competencies acquired through academic sports science courses and the requirements of the relevant jobs in Switzerland. The data for this empirical analysis were drawn from a sample of n = 1054 graduates of different academic sport science programmes at all eight Swiss universities. The results show that academic sport science courses primarily communicate sports-specific expertise and practical sports skills. On the other hand, most graduates consider that the acquisition of interdisciplinary competencies plays a comparatively minor role in sport science education, even though these competencies are felt to be an important requirement in a variety of work-related environments and challenges.

The European Network for Translational Research in Atrial Fibrillation (EUTRAF): objectives and initial results

Atrial fibrillation (AF) is the most common sustained arrhythmia in the general population. As an age-related arrhythmia AF is becoming a huge socio-economic burden for European healthcare systems. Despite significant progress in our understanding of the pathophysiology of AF, therapeutic strategies for AF have not changed substantially and the major challenges in the management of AF are still unmet. This lack of progress may be related to the multifactorial pathogenesis of atrial remodelling and AF that hampers the identification of causative pathophysiological alterations in individual patients. Also, again new mechanisms have been identified and the relative contribution of these mechanisms still has to be established. In November 2010, the European Union launched the large collaborative project EUTRAF (European Network of Translational Research in Atrial Fibrillation) to address these challenges. The main aims of EUTRAF are to study the main mechanisms of initiation and perpetuation of AF, to identify the molecular alterations underlying atrial remodelling, to develop markers allowing to monitor this processes, and suggest strategies to treat AF based on insights in newly defined disease mechanisms. This article reports on the objectives, the structure, and initial results of this network.

Wiring the Vascular Network with Neural Cues: A CNS Perspective

The vascular and the nervous system are responsible for oxygen, nutrient, and information transfer and thereby constitute highly important communication systems in higher organisms. These functional similarities are reflected at the anatomical, cellular, and molecular levels, where common developmental principles and mutual crosstalks have evolved to coordinate their action. This resemblance of the two systems at different levels of complexity has been termed the "neurovascular link." Most of the evidence demonstrating neurovascular interactions derives from studies outside the CNS and from the CNS tissue of the retina. However, little is known about the specific properties of the neurovascular link in the brain. Here, we focus on regulatory effects of molecules involved in the neurovascular link on angiogenesis in the periphery and in the brain and distinguish between general and CNS-specific cues for angiogenesis. Moreover, we discuss the emerging molecular interactions of these angiogenic cues with the VEGF-VEGFR-Delta-like ligand 4 (Dll4)-Jagged-Notch pathway.

ADHERENS JUNCTIONS AND THE ACTOMYOSIN NETWORK REGULATE ORGAN GROWTH BY MODULATING HIPPO PATHWAY ACTIVITY IN DROSOPHILA

Adherens junctions (AJs) and basolateral modules are important for the establishment and maintenance of apico-basal polarity. Loss of AJs and basolateral module members lead to tumor formation, as well as poor prognosis for metastasis. Recently, in mammalian studies it has been shown that loss of either AJ or basolateral module members deregulate Yorkie activity, the downstream transcriptional effector of the Hippo pathway. Importantly, it is unclear if AJ and basolateral components act through the same or parallel mechanisms to regulate Yorkie activity. Here, we dissect how loss of AJ and basolateral components affects Hippo signaling in Drosophila. Surprisingly, while scrib knock-down tissue displays increased reporter activity autonomously, α-cat knock-down tissue shows a cell autonomous decrease and a cell non-autonomous increase of Hippo reporter activity. We provided several lines of evidence to show the differential regulation in polarity protein localizations and oncogenic cooperative overgrowth by AJs and basolateral complexes. Finally, we show that Hippo pathway activity is induced in α-cat and scrib double knocked-down tissue. Taken together, our results provide evidence to show that basolateral modules and AJs act in parallel to modulate Hippo pathway activity. Non-muscle myosin II is an actomyosin component that interacts with the actin. Non-muscle myosin II also interacts with lgl, though the function of this interaction is not clear. Our lab demonstrated that modulating F-actin regulates Hippo pathway activity, and lgl also has been described as a Hippo pathway regulator. Therefore we suspect that myosin II is also involved in Hippo pathway regulation. We first characterized non-muscle Myosin II as a novel tumor suppressor gene by affecting Hippo pathway activity. Upstream regulators of Myosin II, members in the Rho signaling pathway, also displayed similar phenotypes as the Myosin II knock-down tissues. Apoptosis is also induced in myosin II knock-down tissues, however, blocking cell death does not affect myosin II knock-down induced Hippo activation. Our data suggested hyperactivating myosin II induced F-actin accumulation so therefore induces Hippo target activation. Unexpectedly, we also observed that reducing F-actin activity induced Hippo target activation in vivo. These controversial data indicated that actomyosin may regulate the Hippo pathway through multiple mechanisms.

Descriptive analysis of the knowledge network formation in East Asia

This paper shows descriptively how the knowledge network in East Asia has been formed. In addition, the correlation between the knowledge network and economic growth is also examined. Evidence is provided to show that plugging into the knowledge network of developed countries could be a key for increasing innovativeness in a country.

Semantic Technology Recommendation Based on the Analytic Network Process

Semantic technologies have become widely adopted in recent years, and choosing the right technologies for the problems that users face is often a difficult task. This paper presents an application of the Analytic Network Process for the recommendation of semantic technologies, which is based on a quality model for semantic technologies. Instead of relying on expert-based comparisons of alternatives, the comparisons in our framework depend on real evaluation results. Furthermore, the recommendations in our framework derive from user quality requirements, which leads to better recommendations tailored to users’ needs. This paper also presents an algorithm for pairwise comparisons, which is based on user quality requirements and evaluation results.

Study of the II Network as the Compound Slot Equivalent Circuit Model

A combination of Method of Moments (MoM) and compound slot Equivalent Circuit Model for linear array design is presented in this document. From the S Matrix of the single element, the more suitable network for its characterization is analyzed and selected. Then according to the radiation requirements of the desired array, the elements are designed and then properly connected by means of Forward Matching Procedure (FMP), which takes into account impedance matters in order to keep the input matched at the designing frequency. Comparison between HFSS simulations and MoM-FMP results are also presented. First part of this work was introduced in (1)(2) but a summary is included here to make the understanding easier.

Unveiling Protein Functions through the Dynamics of the Interaction Network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes

A Recommendation Framework Based on the Analytic Network Process and its Application in the Semantic Technology Domain

When users face a certain problem needing a product, service, or action to solve it, selecting the best alternative among them can be a dicult task due to the uncertainty of their quality. This is especially the case in the domains where users do not have an expertise, like for example in Software Engineering. Multiple criteria decision making (MCDM) methods are methods that help making better decisions when facing the complex problem of selecting the best solution among a group of alternatives that can be compared according to different conflicting criteria. In MCDM problems, alternatives represent concrete products, services or actions that will help in achieving a goal, while criteria represent the characteristics of these alternatives that are important for making a decision.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

HealthEye: The Social Network : the social network for rare diseases

HealthEye es la red social para unir a personas con la misma enfermedad rara. Este proyecto, es considerado como una startup social, abordando tanto el desarrollo de la plataforma así como el modelo de negocio del proyecto. La parte más complicada del proyecto, sin duda alguna, es encontrar un modelo sostenible para la empresa, y por supuesto, el crear una web de buena calidad para el beneficio del usuario.