A new design principle of robust onion-like networks self-organized in growth

Today's economy, production activity, and our life are sustained by social and technological network infrastructures, while new threats of network attacks by destructing loops have been found recently in network science. We inversely take into account the weakness, and propose a new design principle for incrementally growing robust networks. The networks are self-organized by enhancing interwoven long loops. In particular, we consider the range-limited approximation of linking by intermediations in a few hops, and show the strong robustness in the growth without degrading efficiency of paths. Moreover, we demonstrate that the tolerance of connectivity is reformable even from extremely vulnerable real networks according to our proposed growing process with some investment. These results may indicate a prospective direction to the future growth of our network infrastructures.Comment: 21 pages, 10 figures, 1 tabl

Beyond Covalent Crosslinks: Applications of Supramolecular Gels

Traditionally, gels have been defined by their covalently cross-linked polymer networks. Supramolecular gels challenge this framework by relying on non-covalent interactions for self-organization into hierarchical structures. This class of materials offers a variety of novel and exciting potential applications. This review draws together recent advances in supramolecular gels with an emphasis on their proposed uses as optoelectronic, energy, biomedical, and biological materials. Additional special topics reviewed include environmental remediation, participation in synthesis procedures, and other industrial uses. The examples presented here demonstrate unique benefits of supramolecular gels, including tunability, processability, and self-healing capability, enabling a new approach to solve engineering challenges. Keywords: supramolecular gel; self-assembly; gels; applied soft matte

The design of collaborative projects : Language, metaphor, conversation and the systems approach

This thesis uses a systems approach to develop a model for Collaborative Project Design (CPD). Failure of the software process is the area of concern. The focus of the argument is, however, on the organizational environment of the software process. A central argument is that the analytic tools of standard software development methodologies are inappropriate for systems synthesis. They provide little assistance in coping with the loose complexity that is inherent in the organizational environment in which the software process is embedded. These analytic tools and the engineering language and metaphor which dominate the software process undermine collaboration and disempower business users. CPD was developed to enable viable collaboration that is necessary for the software process to succeed. The purpose of CPD is to provide a systemic model of causal influences and social process in order to guide a project designer when intervening in projects which call for acts of shared creation and/or discovery. CPD was developed through a combination of action research (in projects involving software development and organisational transformation) and theoretical readings focused on the philosophy of meaning, systems thinking, social process and the software process. CPD emphasises that a collaborative project requires careful design of its underlying languages, metaphors and conversations. It identifies three distinct types of conversation, namely communication, dialogue and collaboration. The thesis describes how these conversation types are utilised in transforming a project's network of commitments from loose complexity via shared meaning to cohesive simplicity. Associated with each conversation type is a set of project influences which are developed into a causal influence model in order to depict a collaborative project as a dynamic system of mutually interdependent influences. This causal influence model was used to synthesise the learning from action research and the theoretical readings. An appreciative systems framework was then derived in order to justify a collaborative project as a self-regulating social system and was overlaid onto the causal influence model in order to derive CPD in its final form. CPD proved beneficial when tested in practical projects as a framework to organise a project designer's mind when designing project interventions

Systems Modeling to Predict Mechano-Chemo Interactions In Cardiac Fibrosis

Cardiac fibrosis poses a central challenge in preventing heart failure for patients who have suffered a cardiac injury such as myocardial infarction or aortic valve stenosis. This chronic condition is characterized by a reduction in contractile function through combined hypertrophy and excessive scar formation, and although currently prescribed therapeutics targeting hypertrophy have shown improvements in patient outcomes, pathological fibrosis remains a leading cause of reduced cardiac function for patients long-term. Cardiac fibroblasts play a key role in regulating scar formation during heart failure progression, and interacting biochemical and biomechanical cues within the myocardium guide the activation of fibroblasts and expression of extracellular matrix proteins. While targeted experimental studies of fibroblast activation have elucidated the roles of individual pathways in fibroblast activation, intracellular crosstalk between mechanotransduction and chemotransduction pathways from multiple biochemical cues has largely confounded efforts to control overall cell behavior within the myocardial environment. Computational networks of intracellular signaling can account for complex interactions between signaling pathways and provide a promising approach for identifying influential mechanisms mediating cell behavior. The overarching goal of this dissertation is to improve our understanding of complex signaling in fibroblasts by investigating the role of mechano-chemo interactions in cardiac fibroblast-mediated fibrosis using a combination of experimental studies and systems-level computational models. Firstly, using an in vitro screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical stimuli and mechanical tension, we found that tension modulated cell sensitivity towards biochemical stimuli, thereby altering cell behavior based on the mechanical context. Secondly, using a curated model of fibroblast intracellular signaling, we expanded model topology to include robust mechanotransduction pathways, improved accuracy of model predictions compared to independent experimental studies, and identified mechanically dependent mechanisms-of- ction and mechano-adaptive drug candidates in a post-infarction scenario. Lastly, using an inferred network of fibroblast transcriptional regulation and model fitting to patient-specific data, we showed the utility of model-based approaches in identifying influential pathways underlying fibrotic protein expression during aortic valve stenosis and predicting patient-specific responses to pharmacological intervention. Our work suggests that computational-based approaches can generate insight into influential mechanisms among complex systems, and such tools may be promising for further therapeutic development and precision medicine

Tattoos on Instagram : how the platform connects professionals and consumers

The object of study of this dissertation is the relationship created through Instagram between tattoo artists and consumers. This investigation emphasizes how the relationship allows artists to position themselves in this social network, its advantages, and the reasons that lead a consumer to get a tattoo. The relevance of the same is because few studies investigate the consumption of tattoos in Portugal, the connection that followers create on Instagram with tattoo artists and the impact that this has on the consumption of tattoos. As for the methodological choice, it is a mixed study. That involved the qualitative analysis of the data collected from content analysis and interviews with 6 tattoo artists, as well as the quantitative analysis of the data collected from a questionnaire answered by 567 tattoo consumers on Instagram. It is concluded that tattoo artists connect with their followers on Instagram by sharing personal content that allows identification by consumers. The reasons for consumers to follow tattoo artists are entertainment search and identification, and they interact with them mainly through private messages, answering polls on Instagram stories, comments and likes.O objeto de estudo desta dissertação é a relação criada através do Instagram entre tatuadores e consumidores. Esta investigação explora como a relação permite que os artistas se posicionem nesta rede social, as vantagens que a mesma tem e quais as razões que levam um consumidor marcar uma tatuagem. A pertinência da mesma é justificada por serem poucos os estudos que investigam o consumo de tatuagens em Portugal, sobre a conexão que seguidores criam no Instagram com os tatuadores e o impacto que isso causa no consumo de tatuagens. Quanto à escolha metodológica, trata-se de um estudo misto. Este envolveu a análise qualitativa dos dados recolhidos a partir de uma analise conteúdo e entrevistas feitas a 6 tatuadores, bem como a análise quantitativa dos dados recolhidos a partir de um questionário respondido por 567 consumidores. Conclui-se que os tatuadores conectam-se com seus seguidores no Instagram através da partilha de conteúdo pessoal que permite uma identificação por parte dos consumidores. As razões para os consumidores seguirem os tatuadores são a busca de entretenimento e a identificação, sendo que interagem com os mesmos principalmente através de mensagens privadas, responder a sondagens nas histórias do Instagram, comentários e gostos

Integrating In-Silico Models with In-Vitro Data to Generate Novel Insights into Biological Systems

Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses