Isoperimetric Graph Partitioning for Data Clustering and Image Segmentation

Spectral methods of graph partitioning have been shown to provide a powerful approach to the image segmentation problem. In this paper, we adopt a different approach, based on estimating the isoperimetric constant of an image graph. Our algorithm produces the high quality segmentations and data clustering of spectral methods, but with improved speed and stability.

Cerebellar Learning in an Opponent Motor Controller for Adaptive Load Compensation and Synergy Formation

This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes load compensation while also allowing both coactivation and reciprocal inhibition of sets of antagonist muscles. In particular, we show how synaptic long term depression guided by feedback from muscle stretch receptors can lead to trans-cerebellar gain changes that are load-compensating. It is argued that the same processes help to adaptively discover multi-joint synergies. Simulations of rapid single joint rotations under load illustrates design feasibility and stability.

The importance of interaction strength for food web dynamics and ecosystem functioning

Global biodiversity is eroding at an alarming rate, through a combination of anthropogenic disturbance and environmental change. Ecological communities are bewildering in their complexity. Experimental ecologists strive to understand the mechanisms that drive the stability and structure of these complex communities in a bid to inform nature conservation and management. Two fields of research have had high profile success at developing theories related to these stabilising structures and testing them through controlled experimentation. Biodiversity-ecosystem functioning (BEF) research has explored the likely consequences of biodiversity loss on the functioning of natural systems and the provision of important ecosystem services. Empirical tests of BEF theory often consist of simplified laboratory and field experiments, carried out on subsets of ecological communities. Such experiments often overlook key information relating to patterns of interactions, important relationships, and fundamental ecosystem properties. The study of multi-species predator-prey interactions has also contributed much to our understanding of how complex systems are structured, particularly through the importance of indirect effects and predator suppression of prey populations. A growing number of studies describe these complex interactions in detailed food webs, which encompass all the interactions in a community. This has led to recent calls for an integration of BEF research with the comprehensive study of food web properties and patterns, to help elucidate the mechanisms that allow complex communities to persist in nature. This thesis adopts such an approach, through experimentation at Lough Hyne marine reserve, in southwest Ireland. Complex communities were allowed to develop naturally in exclusion cages, with only the diversity of top trophic levels controlled. Species removals were carried out and the resulting changes to predator-prey interactions, ecosystem functioning, food web properties, and stability were studied in detail. The findings of these experiments contribute greatly to our understanding of the stability and structure of complex natural communities.

The emerging strategic partnership between the European Union and the African Union

Many among the emerging generation of political elites in Africa see the role the European Union (EU) plays in the maintenance of an unprecedented period of peace in Western Europe as an inspirational example of the manner in which the African Union (AU) can contribute to peace and stability in Africa. This doctoral thesis examines security cooperation between the EU and the AU, with a particular focus on the nature and substance of that cooperation. It suggests that despite the establishment of various EU–AU institutions and ties with a role in security policy and cooperation, such security cooperation is limited in substance. This study argues that EU–AU security cooperation is especially constrained by the emergence of alternative partners, most notably China, and by failures of implementation and follow-through. Two case studies, the first dealing with EU–AU cooperation in peacekeeping, and the second addressing the silent water crisis along with the link between water and security, have been analysed in detail to determine the effectiveness and sustainability of the EU–AU partnership. A number of important lessons for regionalism, interregionalism and multilateralism are drawn from the bond between the EU and the AU. This doctoral thesis will prove that, despite an emphasis on the problematic term ‘strategic’ by both EU and AU policymakers, EU–AU cooperation is limited and somewhat lacking in strategic direction. The cooperation between the EU and the AU focuses mainly on EU financial support for AU peacekeeping and specific projects in Africa (e.g. in the water sector), as well as on a limited political dialogue. Nonetheless, the EU–AU link represents the most comprehensive partnership the AU has with any non-African actor. This study will furthermore demonstrate that the United Nations (UN) is an indispensable third-party to their relationship and it is therefore more appropriate to speak of the AU–EU–UN nexus. This doctoral thesis concludes that the AU–EU–UN nexus is an important example of interregionalism in a global context and that such interregionalism is an important emerging part of global governance.

Shifting ranges and conservation challenges for lemurs in the face of climate change.

Geospatial modeling is one of the most powerful tools available to conservation biologists for estimating current species ranges of Earth's biodiversity. Now, with the advantage of predictive climate models, these methods can be deployed for understanding future impacts on threatened biota. Here, we employ predictive modeling under a conservative estimate of future climate change to examine impacts on the future abundance and geographic distributions of Malagasy lemurs. Using distribution data from the primary literature, we employed ensemble species distribution models and geospatial analyses to predict future changes in species distributions. Current species distribution models (SDMs) were created within the BIOMOD2 framework that capitalizes on ten widely used modeling techniques. Future and current SDMs were then subtracted from each other, and areas of contraction, expansion, and stability were calculated. Model overprediction is a common issue associated Malagasy taxa. Accordingly, we introduce novel methods for incorporating biological data on dispersal potential to better inform the selection of pseudo-absence points. This study predicts that 60% of the 57 species examined will experience a considerable range of reductions in the next seventy years entirely due to future climate change. Of these species, range sizes are predicted to decrease by an average of 59.6%. Nine lemur species (16%) are predicted to expand their ranges, and 13 species (22.8%) distribution sizes were predicted to be stable through time. Species ranges will experience severe shifts, typically contractions, and for the majority of lemur species, geographic distributions will be considerably altered. We identify three areas in dire need of protection, concluding that strategically managed forest corridors must be a key component of lemur and other biodiversity conservation strategies. This recommendation is all the more urgent given that the results presented here do not take into account patterns of ongoing habitat destruction relating to human activities.

Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Emergence: a paradigm for robust and scalable distributed applications

Natural distributed systems are adaptive, scalable and fault-tolerant. Emergence science describes how higher-level self-regulatory behaviour arises in natural systems from many participants following simple rulesets. Emergence advocates simple communication models, autonomy and independence, enhancing robustness and self-stabilization. High-quality distributed applications such as autonomic systems must satisfy the appropriate nonfunctional requirements which include scalability, efficiency, robustness, low-latency and stability. However the traditional design of distributed applications, especially in terms of the communication strategies employed, can introduce compromises between these characteristics. This paper discusses ways in which emergence science can be applied to distributed computing, avoiding some of the compromises associated with traditionally-designed applications. To demonstrate the effectiveness of this paradigm, an emergent election algorithm is described and its performance evaluated. The design incorporates nondeterministic behaviour. The resulting algorithm has very low communication complexity, and is simultaneously very stable, scalable and robust.

Molecular biology of cancer: mechanisms, targets, and therapeutics

1: Introduction 2: DNA structure and stability: mutations vs. repair 3: Regulation of gene expression 4: Growth factor signaling and oncogenes 5: The cell cycle 6: Growth inhibition and tumor suppressor genes 7: Apoptosis 8: Stem cells and differentiation 9: Metastasis 10: Infections and inflammation 11: Nutrients, hormones, and gene interactions 12: The Cancer Industry: drug development and clinical trial design 13: Cancer in the future: focus on diagnostics and immunotherapy

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

The Influence of Copper Binding on the Stability of the SCO Protein from Bacillus subtilis

Every aerobic organism expresses cytochrome c oxidase to catalyze reduction of molecular oxygen to water, and takes advantage of this energy releasing reaction to produce an electrochemical gradient used in cellular energy production. The protein SCO (Synthesis of cytochrome c oxidase) is a required assembly factor for the oxidase, conserved across many species. SCO is implicated in the assembly of one of two copper centres (ie., CuA) of cytochrome oxidase. The exact mechanism of SCO’s participation in CuA assembly is not known. SCO has been proposed to bind and deliver copper, or alternatively to act in reductive preparation of the CuA site within the oxidase. In this body of work, the strength and stability of Cu(II) binding to Bacillus subtilis SCO is explored via electronic absorption and fluorescence spectroscopies and by calorimetric methods. An equilibrium dissociation constant (Kd) of 3.5x10-12 M was determined as an upper limit for the BsSCO-Cu(II) interaction, via differential scanning calorimetry. In the first reported case for a SCO homolog, dissociation kinetics of Cu(II) from BsSCO were characterized, and found to be dependent on both ionic strength and the presence of free Cu(II) in solution. Further differential scanning calorimetry experiments performed at high ionic strength support a two-step model of BsSCO and Cu(II) binding. The implications of this model for the BsSCO-Cu(II) interaction are presented in relation to the mechanism of interaction between SCO and the CuA site of cytochrome c oxidase.

Models of open populations with space-limited recruitment: extension of theory and application to the barnacle Chthamalus montagui.

1. Barnacles are a good model organism for the study of open populations with space-limited recruitment. These models are applicable to other species with open supply of new individuals and resource limitation. The inclusion of space in models leads to reductions in recruitment with increasing density, and thus predictions of population size and stability are possible. 2. Despite the potential generality of a demographic theory for open space-limited populations, the models currently have a narrow empirical base. In this study, a model for an open population with space-limited recruitment was extended to include size-specific survival and promotions to any size class. The assumptions of this model were tested using data from a pan-European study of the barnacle Chthamalus montagui Southward. Two models were constructed: a 6-month model and a periodic annual model. Predicted equilibria and their stabilities were compared between shores. 3. Tests of model assumptions supported the extension of the theory to include promotions to any size class. Mortality was found to be size-specific and density independent. Studied populations were open, with recruitment proportional to free space. 4. The 6-month model showed a significant interaction between time and location for equilibrium free space. This may have been due to contrasts in the timing of structuring processes (i.e. creating and filling space) between Mediterranean and Atlantic systems. Integration of the 6-month models into a periodic annual model removed the differences in equilibrium-free space between locations. 5. Model predictions show a remarkable similarity between shores at a European scale. Populations were persistent and all solutions were stable. This reflects the apparent absence of density-dependent mortality and a high adult survivorship in C. montagui. As populations are intrinsically stable, observations of fluctuations in density are directly attributable to variations in the environmental forcing of recruitment or mortality

Prediction- and simulation-error based perceptron training: Solution space analysis and a novel combined training scheme

Previous papers have noted the difficulty in obtaining neural models which are stable under simulation when trained using prediction-error-based methods. Here the differences between series-parallel and parallel identification structures for training neural models are investigated. The effect of the error surface shape on training convergence and simulation performance is analysed using a standard algorithm operating in both training modes. A combined series-parallel/parallel training scheme is proposed, aiming to provide a more effective means of obtaining accurate neural simulation models. Simulation examples show the combined scheme is advantageous in circumstances where the solution space is known or suspected to be complex. (c) 2006 Elsevier B.V. All rights reserved.

Simulation study of Pd submonolayer films on Au(hkl) and Pt(hkl) and their relationship to underpotential deposition

The structure and stability of palladium adlayers on Au(hkl) and Pt(hkl) were studied at different coverage degrees by means of Monte Carlo simulations using the interatomic potentials of the embedded atom model. In all cases the Pd films were found to grow epitaxially and pseudomorphically with the crystallographic orientation of the substrate. The differences and similarities of the adlayer with the substrate were analyzed.