Spatial and Temporal Models of Jomon Settlement

The Jomon culture is a tradition of complex hunter-gatherers which rose in the Japanese archipelago at the end of the Pleistocene (ca. 13,000 cal BP) and lasted until the 3rd millennium cal BP. Recent studies increasingly suggest how this long cultural persistence was characterised by repeated episodes of change in settlement pattern, primarily manifested as cyclical transitions between nucleated and dispersed distributions. Although it has been suggested that these events correlate with population dynamics, shifts in subsistence strategies, and environmental change, to date there have been very few attempts to provide a quantitative analysis of spatio-temporal change in Jomon settlement and its possible causes. This thesis is an attempt to fill that lacuna by adopting a twin-track approach to the problem. First, two case studies from central Japan have been examined using a novel set of methods, which have been specifically designed to handle the intrinsic chronological uncertainty which characterises most prehistoric data. This facilitated the application of a probabilistic framework for quantitatively assessing the available information, making it possible to identify alternating phases of nucleated and dispersed pattern during a chronological interval between 7000 and 3300 cal BP. Second, computer simulation (by means of an agent-based model) has been used to carry out a formal inquiry into the possible underlying processes that might have triggered the observed changes in the settlement pattern. The aim of this simulation exercise was two-fold. First, it has been used as a theory-building tool, combining several models from behavioural ecology and cultural transmission theory in order to provide explicit expectations in relation to the presence and absence of environmental disturbances. Second, the outcome of the simulation has been used as a template for linking the observed patterns to possible underlying socio-ecological processes suggested by the agent-based model. This endeavour has shown how some of the largest changes in the empirically observed settlement patterns can be simulated as emerging from the internal dynamics of the system rather than necessarily being induced by external changes in the environment

Combining Network Modeling and Experimental Approaches to Predict Drug Combination Responses

Cancer is a lethal disease and complex at multiple levels of cell biology. Despite many advances in treatments, many patients do not respond to therapy. This is owing to the complexity of cancer-genetic variability due to mutations, the multi-variate biochemical networks within which drug targets reside and existence and plasticity of multiple cell states. It is generally understood that a combination of drugs is a way to address the multi-faceted drivers of cancer and drug resistance. However, the sheer number of testable combinations and challenges in matching patients to appropriate combination treatments are major issues. Here, we first present a general method of network inference which can be applied to infer biological networks. We apply this method to infer different kinds of networks in biological levels where cancer complexity resides-a biochemical network, gene expression and cell state transitions. Next, we focus our attention on glioblastoma and with pharmacological and biological considerations, obtain a ranked list of important drug targets in glioblastoms. We perform drug dose response experiments for 22 blood brain barrier penetrant drugs against 3 glioblastoma cell lines. These methods and experimental results inform a construction of a temporal cell state model to predict and experimentally validate combination treatments for certain drugs. We improve an experimental method to perform high throughput western blots and apply the method to discover biochemical interactions among some important proteins involved in temporal cell state transitions. Lastly, we illustrate a method to investigate potential resistance mechanisms in genome scale proteomic data. We hope that methods and results presented here can be adapted and improved upon to help in the discovery of biochemical interactions, capturing cell state transitions and ultimately help predict effective combination therapies for cancer

Paradoxes of Interactivity

Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. »Paradoxes of Interactivity« brings together reflections on »interactivity« from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound

Paradoxes of interactivity: perspectives for media theory, human-computer interaction, and artistic investigations

Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. "Paradoxes of Interactivity" brings together reflections on "interactivity" from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound