Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Using spatiotemporal correlative niche models for evaluating the effects of climate change on mountain pine beetle

Includes bibliographical references.2015 Summer.Over the last decade western North America has experienced the largest mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in recorded history and Rocky Mountain forests have been severely impacted. Although bark beetles are indigenous to North American forests, climate change has facilitated the beetle’s expansion into previously unsuitable habitats. I used three correlative niche models (MaxEnt, Boosted Regression Trees, and Generalized Linear Models) to estimate: (i) the current potential distribution of the beetle in the U.S. Rocky Mountain region, (ii) how this extent has changed since historical outbreaks in the 1960s and 1970s, and (iii) how the potential distribution may be expected to change under future climate scenarios. Additionally, I evaluated the temporal transferability of the niche models by forecasting historical models and testing the model predictions using temporally independent outbreak data from the current outbreak. My results indicated that there has been a significant expansion of climatically suitable habitat over the past 50 years and that much of this expansion corresponds with an upward shift in elevation across the study area. Furthermore, my models indicate that drought was a more prominent driver of current outbreak than temperature, which suggests a change in the climatic signature between historical and current outbreaks. The current climatic niche of the mountain pine beetle includes increased precipitation, colder winter temperatures, and a later spring than the historical climatic niche, which reflects a shift into higher elevation habitats. Projections under future conditions suggest that there will be a large reduction in climatically suitable habitat for the beetle and that high-elevation forests will continue to become more susceptible to outbreak. While all three models generated reasonable predictions (AUC = 0.85 - 0.87), the generalized linear model correctly predicted a higher percentage of current outbreak localities when trained on historical data. My findings suggest that projects aiming to reduce omission error in estimates of future species responses may have greater predictive success with simpler, generalized models

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

Structure of the trypanosome transferrin receptor and insights into ligand binding and therapeutic strategies

African trypanosomes are extracellular protozoan pathogens that have evolved a complex and sophisticated interface with hosts to acquire nutrients and protect against the host immune system. The variant surface glycoprotein (VSG) forms a dense and dynamic protective coat, which serves to defend against the immunoglobulin-mediated host response. In addition, a unique set of receptors mediate uptake of host macromolecules and must comply with recognising ligands from a broad host range whilst also avoiding immune detection. Host transferrin (Tf) is acquired by trypanosomes through the expression of a transferrin receptor (TfR), which does not share homology with the host TfR. Multiple diverse copies of the TfR are present in the trypanosome genome but only one is expressed at a time. Several hypotheses revolve around the evolutionary advantage of multiplication and diversification of the receptor, falling into two main categories. Either the TfR repertoire serves to accommodate different Tfs from the wide vertebrate host range, or variation has arisen as part of an immune evasion strategy. To elucidate the complexities surrounding the receptor, two different TfRs were expressed as recombinant proteins to study receptor-ligand interactions at the molecular level. Biophysical analyses of binding interactions provided no evidence to support the hypothesis that the receptor repertoire had evolved to favour host promiscuity. Instead, a single receptor was capable of binding Tf from multiple different mammals, highlighting the lack of requisite to evolve species-specific receptors. Structural determination of the trypanosome TfR in conjunction with sequence variation mapping provided further evidence. The structure revealed a predominantly conserved Tf binding site, while variation and N-linked glycans resided in the surrounding accessible regions at the apical surface of the receptor, indicating that receptor evolution was likely driven by an immune evasion strategy. The findings presented in this thesis have served to further our understanding of the molecular architecture of the trypanosome cell surface by providing structural and biophysical data to define a complex host-parasite interaction. Therapeutic strategies have also been adapted through advances in our knowledge of the relationship between trypanosome and host. Finally, the trypanosome TfR is a remarkable example of evolutionary drift within a receptor, propagated by the advantage conferred by immune avoidance, and restricted by functional requirements of ligand uptake. Thus, the TfR repertoire has likely derived from an antigenic variation strategy to promote long-term persistence in the host

Modes of production, metabolism and resilience: toward a framework for the analysis of complex social-ecological systems

The field of environmental sociology has undergone drastic change in recent decades, in context of a broader reconfiguration of the terrain of sociological theory and practice. Systems-based approaches to the study of human society, located at the interface between the natural and social sciences have since yielded to a fragmentary body of theory and practice. Subsequent developments such as the emergence of actor network theory, linguistic constructivism and epistemic relativism, have sought not only to question the status of scientific discourse as immutable authority, but also the legitimacies of positivism and macro-theoretical modeling as tenable research programs. This thesis suggests that much of this critique is misdirected, informed as it is by false dichotomies of theory and method which empahsise the separatism of the social, and the difficulty of normative analysis. Over the past twenty years, sociologists have begun to re-engage with systemic theory, albeit with a plethora of new anti-reductionist informants rooted in epistemologies of emergentism, complexity and critical realism. Parallel developments in Marxian ecological thought and human ecology offer further conceptual complementarities and points of dialogue, with which to develop new methodologies for the study of human collectives as ‗social-ecological systems‘. The objectives of this work are thus twofold; (1) to advance an alternative basis for theory and practice in environmental sociology, drawing upon the informants of complexity theory, resilience-based human ecology, and Marx‘s concepts of mode of production and metabolic rift; (2) to contribute to this largely theoretical body of knowledge, by operationalising the preceding informants within a specific case study; that of communal farming, or the 'rundale system‘, in nineteenth century Ireland. The ecological dynamics of the rundale system are thus explored through the imposition of a range of quantitative, archival and comparative methods, as an exercise in the explanatory capacities of the investigative framework developed throughout this work. This methodology rejects existing explanatory models which emphasise the role of 'prime movers‘ in the generation of differential ecological outcomes, toward an account which emphasises both macro-structural complexity, and the augmentation of adaptive capacity from below

Short interval change in hepatitis C hypervariable region 1 in chronic infection. Are there treatment windows in the envelope?

Hepatitis C Virus (HCV), an RNA virus, is one of the leading causes of cirrhosis worldwide and, remains the leading indication for orthoptic liver transplantation in the United States. Dual treatment with pegylated interferon and ribavirin has until 2010 been the mainstay of treatment. The emergence of newer agents with direct activity against specific virus proteins has revolutionised HCV treatment but, the high cost of these medications are likely to prevent universal access, particularly in developing countries and, strategies to optimise response to cheaper combination treatments are required. The Irish Hepatitis C outcomes research network (ICORN) has proposed a target of 2025 for the complete eradication of Hepatitis C from Ireland. HCV replicates in an error prone fashion resulting in mutant progeny known as quasispecies(QS), thought to form an important mechanism of host immune evasion in the establishment and maintenance of chronic infection, which develops in 50-80% of those acutely infected. HCV has three hypervariable regions (sections of the virus genome that appear to tolerate higher substitution rates) and one of these, Hypervariable region 1 (HVR1) has been recognised as a major target of the adaptive immune response. HVR1 quasispecies complexity and diversity have been implicated as predictive of response to dual therapy. Little, however, is known about the natural history of these parameters in chronic infection. We discuss evolutionary concepts and how they apply to quasispecies and hypothesise how viruses might select a setting appropriate mutation rate in order to optimise adaptation, advancing the theory of replicative homeostasis. We prospectively study 23 patients with chronic HCV infections and, differing degrees of liver fibrosis fortnightly for a 16 week period prior to commencement of treatment. Using amplicon sequencing, cloning and next generation sequencing we explore the behaviour of HVR1 QS, establishing the utility of each technique in describing QS change. We identify variable and unpredictable HVR1 change in our cloning data which precludes the use of these metrics in pre treatment prediction models. HVR1 change is far greater in non cirrhotic patients and the transition to cirrhosis appears to be associated with a change from positive to purifying selection. Using molecular clock techniques we illustrate differing substitution rates within HVR1 among cirrhotic and non cirrhotic patients. We identify, by including an additional retrospective sample, that the patterns we describe are sustained over prolonged periods and further clarify the mode and tempo of HVR1 change by estimating the substitution rates. Using next generation sequencing techniques we identify similar patterns of HCV change when compared with our cloning data. However, the sequence depth provided permits the description of time specific network of HVR1 clones, all connected by a single amino acid substitution to a central node. By separating our samples into immunoglobulin bound and free fractions we describe the importance of host immune mediated change driving the changes seen in our pyrosequencing and cloning data. Finally, using known viral and host molecular markers predictive of treatment response we explore unsuccessfully for models predictive of treatment response

Modeling uncertainty in pathogen transmission and evolution for infectious disease management

Public health practitioners rely on epidemiological case count and molecular sequence data for making decisions regarding the prevention, control, and treatment of infectious disease. The relationship between what practitioners can observe and what they wish to know, (i.e. the epidemiological or evolutionary state of the population) can be uncertain because of gaps or biases in the data collection and interpretation steps. In this dissertation, I integrate simulations of disease dynamics with statistical frameworks to link such observational data with probabilistic statements about the range of underlying possible outcomes. Chapter 2 addresses the need for real-time estimates of local Zika epidemic risk during an unfolding outbreak to inform county-specific public health response plans. In this chapter, I present a quantitative framework for estimating real-time ZIKV risk that captures uncertainty in case reporting, importations, and vector-human transmission dynamics. I find that accurate estimates of the case reporting rate can reduce the uncertainty in perceived epidemic risk. In addition, local differences in both the environmental suitability for ZIKV transmission and in the numbers of new cases arriving influence how long a policy maker can wait before implementing response efforts to curb a growing epidemic. In Chapter 3, I address the theoretical and practical questions of how early can population level antigenic evolution of seasonal influenza A/H3N2 be predicted, and what are the best data metrics to inform such predictive models. Using a detailed simulation model of seasonal influenza transmission and evolution, I fit predictive logistic regression models of antigenic variant success to epidemiological and population genetic predictors derived from theoretical case and molecular data. The results show that the relative transmission rates of newly emerging influenza variants can robustly indicate future epidemic threats, up to 10 months prior to their widespread expansion. This chapter demonstrates that the early detection of emerging influenza viruses is limited by a tight race between the typical dynamics of antigenic turnover and the annual timeline for vaccine development. Chapter 4 examines how complex evolutionary processes, such as recombination and latency, leave detectable signals in HIV-1 molecular data when analyzed using methods that assume the absence of these processes. First, I develop a new method for simulating the evolutionary history of a set of molecular sequence samples using ancestral recombination graphs (ARG). Next, I use a new statistical framework for comparing simulated and observed HIV-1 trees. I find evidence that the intensity of within-host evolutionary processes is detectable in binary trees constructed using hierarchical clustering methods. Furthermore, the latent reservoir size is likely to differ between individual patients. This model represents an important advance in the realism of HIV within-in host evolutionary modeling. Altogether, the results in this dissertation demonstrate how effective infectious disease management can be improved by using an interdisciplinary approach of computational epidemiology and statistics to strategically think about complex data collection and interpretation questions.Ecology, Evolution and Behavio