Neurogenic inflammation after traumatic brain injury and its potentiation of classical inflammation

Background: The neuroinflammatory response following traumatic brain injury (TBI) is known to be a key secondary injury factor that can drive ongoing neuronal injury. Despite this, treatments that have targeted aspects of the inflammatory pathway have not shown significant efficacy in clinical trials. Main body: We suggest that this may be because classical inflammation only represents part of the story, with activation of neurogenic inflammation potentially one of the key initiating inflammatory events following TBI. Indeed, evidence suggests that the transient receptor potential cation channels (TRP channels), TRPV1 and TRPA1, are polymodal receptors that are activated by a variety of stimuli associated with TBI, including mechanical shear stress, leading to the release of neuropeptides such as substance P (SP). SP augments many aspects of the classical inflammatory response via activation of microglia and astrocytes, degranulation of mast cells, and promoting leukocyte migration. Furthermore, SP may initiate the earliest changes seen in blood-brain barrier (BBB) permeability, namely the increased transcellular transport of plasma proteins via activation of caveolae. This is in line with reports that alterations in transcellular transport are seen first following TBI, prior to decreases in expression of tight-junction proteins such as claudin-5 and occludin. Indeed, the receptor for SP, the tachykinin NK1 receptor, is found in caveolae and its activation following TBI may allow influx of albumin and other plasma proteins which directly augment the inflammatory response by activating astrocytes and microglia. Conclusions: As such, the neurogenic inflammatory response can exacerbate classical inflammation via a positive feedback loop, with classical inflammatory mediators such as bradykinin and prostaglandins then further stimulating TRP receptors. Accordingly, complete inhibition of neuroinflammation following TBI may require the inhibition of both classical and neurogenic inflammatory pathways.Frances Corrigan, Kimberley A. Mander, Anna V. Leonard and Robert Vin

An integrated methodology for modelling complex adaptive production networks

Adaptation and learning are the most crucial skills in the survival of any complex system - the former one emphasizing the ability to perform structural reorganization and the latter one the use of previously available information - to reflect on the endlessly changing environment the particular system is embedded in. Humans are such complex systems and also manmade ones that humans manage by the aid of cooperation, science and the multitude of automated tools such as computers, robots, vehicles and their combinations. The survival fitness of individuals, organizations, societies and mankind itself depends on the successful management of the adaptation and learning process that often involves the changing of the environment. In this interplay between man and nature it is crucial to gather useful knowledge of explanatory and predictive power in the - Aristotelian - form of science and metaphors. In addition to these, computers have provided a third form or language for knowledge gathering and representation since the middle of the XXth century. The success of a system of knowledge - a theory - largely depends on the integrated application of these knowledge acquisition methods and is measured by the fitness and survival of its users. Since scientific methods are typically limited in scope, metaphors are used to bridge the gaps and connect seemingly distinct fields. The general aim of this thesis is to contribute to the area of complex adaptive systems research - in particular complex adaptive production networks - by integrating scientific, metaphoric and computational knowledge in a methodology to complement more traditional and specialized approaches such as mathematical equation based modelling, computer simulation techniques and management methods. Building synthetic, agentbased simulation models is only part of this endeavor, providing a media for repeatable experiments that point to various scenarios leading to chaotic behavior, inflection points and bifurcations. Since research in the area of agentbased modelling and complex adaptive systems often concentrates on building software and running simulations, the methodology developed in this work is mainly concerned about the bigger picture that includes not only a basic software library but a scientific and philosophical framework that integrates knowledge gathering techniques and languages and helps to navigate in the challenging area of complex systems by exploring limitations and opportunities systematically. Keywords: agentbased modelling and simulation, complex adaptive systems, dissipative structures, evolutionary computation, methodology development, production networks

An integrated methodology for modelling complex adaptive production networks

Adaptation and learning are the most crucial skills in the survival of any complex system - the former one emphasizing the ability to perform structural reorganization and the latter one the use of previously available information - to reflect on the endlessly changing environment the particular system is embedded in. Humans are such complex systems and also manmade ones that humans manage by the aid of cooperation, science and the multitude of automated tools such as computers, robots, vehicles and their combinations. The survival fitness of individuals, organizations, societies and mankind itself depends on the successful management of the adaptation and learning process that often involves the changing of the environment. In this interplay between man and nature it is crucial to gather useful knowledge of explanatory and predictive power in the - Aristotelian - form of science and metaphors. In addition to these, computers have provided a third form or language for knowledge gathering and representation since the middle of the XXth century. The success of a system of knowledge - a theory - largely depends on the integrated application of these knowledge acquisition methods and is measured by the fitness and survival of its users. Since scientific methods are typically limited in scope, metaphors are used to bridge the gaps and connect seemingly distinct fields. The general aim of this thesis is to contribute to the area of complex adaptive systems research - in particular complex adaptive production networks - by integrating scientific, metaphoric and computational knowledge in a methodology to complement more traditional and specialized approaches such as mathematical equation based modelling, computer simulation techniques and management methods. Building synthetic, agentbased simulation models is only part of this endeavor, providing a media for repeatable experiments that point to various scenarios leading to chaotic behavior, inflection points and bifurcations. Since research in the area of agentbased modelling and complex adaptive systems often concentrates on building software and running simulations, the methodology developed in this work is mainly concerned about the bigger picture that includes not only a basic software library but a scientific and philosophical framework that integrates knowledge gathering techniques and languages and helps to navigate in the challenging area of complex systems by exploring limitations and opportunities systematically. Keywords: agentbased modelling and simulation, complex adaptive systems, dissipative structures, evolutionary computation, methodology development, production networks