Reliability Analysis of Social Networks

The primary focus of this dissertation is on the quantification of actor interaction and the dissemination of information through Social networks. Social networks have long been used to model the interactions between people in various Social and professional contexts. These networks allow for the explicit modeling of the complex interrelations between relevant individuals within an organization and the role they play in the decision making process. This dissertation considers Social networks represented as network flow models in which actors have the ability to provide some level of influence over other actors within the network. The models developed incorporate performance metrics and reliability analysis established in the multi-state reliability literature to gain insights into organizational behavior. After a brief introduction, Chapter 2 provides a survey of the relevant literature on several topics of interest within this dissertation. In Chapter 3, actor criticality findings using traditional Social network analysis are compared to those obtained via multi-state reliability importance measures. Chapter 4 extends the model developed in Chapter 3 to consider that an actor\u27s Social interaction and level of influence within the organization are not only multi-valued and stochastic in nature but also a function of the interactions with its neighbors. A Monte Carlo simulation model is presented to evaluate the reliability of the network, and network reliability is evaluated under various influence communication rules. In Chapter 5, a hierarchical network structure is investigated where actors are arranged in layers and communication exists between layers. A probability mass function is developed to compute the expected level of influence at the target nodes as a function of the existing communication paths within the network. An illustrative example is used to demonstrate the effects on expected influence at the target as connections are either added or removed and when the uncertainty associated with an actor\u27s influence level is removed. Finally, in Chapter 6, a methodology is developed for eliciting the probabilities associated with the influence levels used in the network analysis of Chapters 3 - 5

Multi-Objective Model to Improve Network Reliability Level under Limited Budget by Considering Selection of Facilities and Total Service Distance in Rescue Operations

Sudden disasters may damage facilities, transportation networks and other critical infrastructures, delay rescue and bring huge losses. Facility selection and reliable transportation network play an important role in emergency rescue. In this paper, the reliability level between two points in a network is defined from the point of view of minimal edge cut and path, respectively, and the equivalence of these two definitions is proven. Based on this, a multi-objective optimization model is proposed. The first goal of the model is to minimize the total service distance, and the second goal is to maximize the network reliability level. The original model is transformed into a model with three objectives, and the three objectives are combined into one objective by the method of weighting. The model is applied to a case, and the results are analyzed to verify the effectiveness of the model

Development of a Parallel BAT and Its Applications in Binary-state Network Reliability Problems

Various networks are broadly and deeply applied in real-life applications. Reliability is the most important index for measuring the performance of all network types. Among the various algorithms, only implicit enumeration algorithms, such as depth-first-search, breadth-search-first, universal generating function methodology, binary-decision diagram, and binary-addition-tree algorithm (BAT), can be used to calculate the exact network reliability. However, implicit enumeration algorithms can only be used to solve small-scale network reliability problems. The BAT was recently proposed as a simple, fast, easy-to-code, and flexible make-to-fit exact-solution algorithm. Based on the experimental results, the BAT and its variants outperformed other implicit enumeration algorithms. Hence, to overcome the above-mentioned obstacle as a result of the size problem, a new parallel BAT (PBAT) was proposed to improve the BAT based on compute multithread architecture to calculate the binary-state network reliability problem, which is fundamental for all types of network reliability problems. From the analysis of the time complexity and experiments conducted on 20 benchmarks of binary-state network reliability problems, PBAT was able to efficiently solve medium-scale network reliability problems

Evaluating multiple criteria for species delimitation: an empirical example using Hawaiian palms (Arecaceae: Pritchardia)

<p>Abstract</p> <p>Background</p> <p>Robust species delimitations are fundamental for conservation, evolutionary, and systematic studies, but they can be difficult to estimate, particularly in rapid and recent radiations. The consensus that species concepts aim to identify evolutionarily distinct lineages is clear, but the criteria used to distinguish evolutionary lineages differ based on the perceived importance of the various characteristics of evolving populations. We examined three different species-delimitation criteria (monophyly, absence of genetic intermediates, and diagnosability) to determine whether currently recognized species of Hawaiian <it>Pritchardia </it>are distinct lineages.</p> <p>Results</p> <p>Data from plastid and nuclear genes, microsatellite loci, and morphological characters resulted in various levels of lineage subdivision that were likely caused by differing evolutionary rates between data sources. Additionally, taxonomic entities may be confounded because of the effects of incomplete lineage sorting and/or gene flow. A coalescent species tree was largely congruent with the simultaneous analysis, consistent with the idea that incomplete lineage sorting did not mislead our results. Furthermore, gene flow among populations of sympatric lineages likely explains the admixture and lack of resolution between those groups.</p> <p>Conclusions</p> <p>Delimiting Hawaiian <it>Pritchardia </it>species remains difficult but the ability to understand the influence of the evolutionary processes of incomplete lineage sorting and hybridization allow for mechanisms driving species diversity to be inferred. These processes likely extend to speciation in other Hawaiian angiosperm groups and the biota in general and must be explicitly accounted for in species delimitation.</p

Cell-free expression and molecular modeling of the γ-secretase complex and G-protein-coupled receptors

Alzheimer’s disease (AD), which was first reported more than a century ago by Alhzeimer, is one of the commonest forms of dementia which affects >30 million people globally (>8 million in Europe). The origin and pathogenesis of AD is poorly understood and there is no cure available for the disease. AD is characterized by the accumulation of senile plaques composed of amyloid beta peptides (Ab 37-43) which is formed by the gamma secretase (GS) complex by cleaving amyloid precursor protein. Therefore GS can be an attractive drug target. Since GS processes several other substrates like Notch, CD44 and Cadherins, nonspecific inhibition of GS has many side effects. Due to the lack of crystal structure of GS, which is attributed to the extreme difficulties in purifying it, molecular modeling can be useful to understand its architecture. So far only low resolution cryoEM structures of the complex has been solved which only provides a rough structure of the complex at low 12-15 A resolution Furthermore the activity of GS in vitro can be achieved by means of cell-free (CF) expression. GS comprises catalytic subunits namely presenilins and supporting elements containing Pen-2, Aph-1 and Nicastrin. The origin of AD is hidden in the regulated intramembrnae proteolysis (RIP) which is involved in various physiological processes and also in leukemia. So far growth factors, cytokines, receptors, viral proteins, cell adhesion proteins, signal peptides and GS has been shown to undergo RIP. During RIP, the target proteins undergo extracellular shredding and intramembrane proteolysis. This thesis is based on molecular modeling, molecular dynamics (MD) simulations, cell-free (CF) expression, mass spectrometry, NMR, crystallization, activity assay etc of the components of GS complex and G-protein coupled receptors (GPCRs). First I validated the NMR structure of PS1 CTF in detergent micelles and lipid bilayers using coarse-grained MD simulations using MARTINI forcefield implemented in Gromacs. CTF was simulated in DPC micelles, DPPC and DLPC lipid bilayer. Starting from random configuration of detergent and lipids, micelle and lipid bilyer were formed respectively in presence of CTF and it was oriented properly to the micelle and bilyer during the simulation. Around DPC molecules formed micelle around CTF in agreement of the experimental results in which 80-85 DPC molecules are required to form micelles. The structure obtained in DPC was similar to that of NMR structure but differed in bilayer simulations showed the possibility of substrate docking in the conserved PAL motif. Simulations of CTF in implicit membrane (IMM1) in CHAMM yielded similar structure to that from coarse grained MD. I performed cell-free expression optimization, crystallization and NMR spectroscopy of Pen-2 in various detergent micelles. Additionally Pen-2 was modeled by a combination of rosetta membrane ab-initio method, HHPred distant homology modeling and incorporating NMR constraints. The models were validated by all atom and coarse grained MD simulations both in detergent micelles and POPC/DPPC lipid bilayers using MARTINI forcefield. GS operon consisting of all four subunits was co-expressed in CF and purified. The presence of of GS subunits after pull-down with Aph-1 was determined by western blotting (Pen-2) and mass spectrometry (Presenilin-1 and Aph-1). I also studied interactions of especially PS1 CTF, APP and NTF by docking and MD. I also made models and interfaces of Pen-2 with PS1 NTF and checked their stability by MD simulations and compared with experimental results. The goal is to model the interfaces between GS subunits using molecular modeling approaches based on available experimental data like cross-linking, mutations and NMR structure of C-terminal fragment of PS1 and transmembrane part of APP. The obtained interfaces of GS subunits may explain its catalysis mechanism which can be exploited for novel lead design. Due to lack of crystal/NMR structure of the GS subunits except the PS1 CTF, it is not possible to predict the effect of mutations in terms of APP cleavage. So I also developed a sequence based approach based on machine learning using support vector machine to predict the effect of PS1 CTF L383 mutations in terms of Aβ40/Aβ42 ratio with 88% accuracy. Mutational data derived from the Molgen database of Presenilin 1 mutations was using for training. GPCRs (also called 7TM receptors) form a large superfamily of membrane proteins, which can be activated by small molecules, lipids, hormones, peptides, light, pain, taste and smell etc. Although 50% of the drugs in market target GPCRs , only few are targeted therapeutically. Such wide range of targets is due to involvement of GPCRs in signaling pathways related to many diseases i.e. dementia (like Alzheimer's disease), metabolic (like diabetes) including endocrinological disorders, immunological including viral infections, cardiovascular, inflammatory, senses disorders, pain and cancer. Cannabinoid and adrenergic receptors belong to the class A (similar to rhodopsin) GPCRs. Docking of agonists and antagonists to CB1 and CB2 cannabinoid receptors revealed the importance of a centrally located rotamer toggle switch, and its possible role in the mechanism of agonist/antagonist recognition. The switch is composed of two residues, F3.36 and W6.48, located on opposite transmembrane helices TM3 and TM6 in the central part of the membranous domain of cannabinoid receptors. The CB1 and CB2 receptor models were constructed based on the adenosine A2A receptor template. The two best scored conformations of each receptor were used for the docking procedure. In all poses (ligand-receptor conformations) characterized by the lowest ligand-receptor intermolecular energy and free energy of binding the ligand type matched the state of the rotamer toggle switch: antagonists maintained an inactive state of the switch, whereas agonists changed it. In case of agonists of β2AR, the (R,R) and (S,S) stereoisomers of fenoterol, the molecular dynamics simulations provided evidence of different binding modes while preserving the same average position of ligands in the binding site. The (S,S) isomer was much more labile in the binding site and only one stable hydrogen bond was created. Such dynamical binding modes may also be valid for ligands of cannabinoid receptors because of the hydrophobic nature of their ligand-receptor interactions. However, only very long molecular dynamics simulations could verify the validity of such binding modes and how they affect the process of activation. Human N-formyl peptide receptors (FPRs) are G protein-coupled receptors (GPCRs) involved in many physiological processes, including host defense against bacterial infection and resolving inflammation. The three human FPRs (FPR1, FPR2 and FPR3) share significant sequence homology and perform their action via coupling to Gi protein. Activation of FPRs induces a variety of responses, which are dependent on the agonist, cell type, receptor subtype, and also species involved. FPRs are expressed mainly by phagocytic leukocytes. Together, these receptors bind a large number of structurally diverse groups of agonistic ligands, including N-formyl and nonformyl peptides of different composition, that chemoattract and activate phagocytes. For example, N-formyl-Met-Leu-Phe (fMLF), an FPR1 agonist, activates human phagocyte inflammatory responses, such as intracellular calcium mobilization, production of cytokines, generation of reactive oxygen species, and chemotaxis. This ligand can efficiently activate the major bactericidal neutrophil functions and it was one of the first characterized bacterial chemotactic peptides. Whereas fMLF is by far the most frequently used chemotactic peptide in studies of neutrophil functions, atomistic descriptions for fMLF-FPR1 binding mode are still scarce mainly because of the absence of a crystal structure of this receptor. Elucidating the binding modes may contribute to designing novel and more efficient non-peptide FPR1 drug candidates. Molecular modeling of FPR1, on the other hand, can provide an efficient way to reveal details of ligand binding and activation of the receptor. However, recent modelings of FPRs were confined only to bovine rhodopsin as a template. To locate specific ligand-receptor interactions based on a more appropriate template than rhodopsin we generated the homology models of FPR1 using the crystal structure of the chemokine receptor CXCR4, which shares over 30% sequence identity with FPR1 and is located in the same γ branch of phylogenetic tree of GPCRs (rhodopsin is located in α branch). Docking and model refinement procedures were pursued afterward. Finally, 40 ns full-atom MD simulations were conducted for the Apo form as well as for complexes of fMLF (agonist) and tBocMLF (antagonist) with FPR1 in the membrane. Based on locations of the N- and C-termini of the ligand the FPR1 extracellular pocket can be divided into two zones, namely, the anchor and activation regions. The formylated M1 residue of fMLF bound to the activation region led to a series of conformational changes of conserved residues. Internal water molecules participating in extended hydrogen bond networks were found to play a crucial role in transmitting the agonist-receptor interactions. A mechanism of initial steps of the activation concurrent with ligand binding is proposed. I accurately predicted the structure and ligand binding pose of dopamine receptor 3 (RMSD to the crystal structure: 2.13 Å) and chemokine receptor 4 (CXCR4, RMSD to the crystal structure 3.21 Å) in GPCR-Dock 2010 competition. The homology model of the dopamine receptor 3 was 8 th best overall in the competition

Causal failures and cost-effective edge augmentation in networks

Node failures have a terrible effect on the connectivity of the network. In traditional models, the failures of nodes affect their neighbors and may further trigger the failures of their neighbors, and so on. However, it is also possible that node failures would indirectly cause the failure of nodes that are not adjacent to the failed one. In a power grid, generators share the load. Failure of one generator induces extra load on other generators in the network, which could further trigger their failures. We call such failures causal failures. In this dissertation, we consider the impact of causal failures on multiple aspects of one network. More specifically, we list the content as follows. • In Chapter 1, we introduce basic concepts of networks and graphs, classical models of failures and formally define causal failures in a given network. • Chapter 2 addresses the network’s robustness and aims to find the maximum number of causal failures while maintaining a connected component with a size of at least a given integer. More specifically, we are looking into the number of causal node failures we can tolerate yet have most of the system connected with α being used to parametrize. • Chapter 3 deals with vulnerability, wherein we aim to find the minimum number of causal failures such that there are at least k connected components remaining. We are looking for the set of causal failures that will result in the network being disconnected into k or more components. • In Chapter 4, we consider causal node failures occurring in a cascading manner. Cascading causal node failures affect communication within nodes, which is dependent on the paths that connect them. Therefore, in this context of the cascading causal failure model, we study the impact of cascading causal failures on the distance between a pair of nodes in the network. More precisely, given a network G, a set of causal failures (containing possible cascading failures), a pair of nodes s and t, and a constant α ≥ 1, we would like to determine the maximum number of causal failures that can be applied (meaning that the nodes in the causal failures are removed), such that in the resulting network G′, dG′ (s, t) ≤ α × dG(s, t), where dG(s, t) and dG′ (s, t) are the distance between nodes s and t in the networks G and G′, respectively. • In Chapter 5, we consider causal edge failures in flow networks and investigate the impact of causal edge failures on flow transmission. We formulate an optimization problem to find the maximum number of causal edge failures after which the flow network can still deliver d units from source node s to terminal node t. • In Chapter 6, we consider edge-weighted network augmentation when facing causal failures. We look for a set of edges with minimum weight such that the network maintains an α-giant component when applying each causality individually. We show that the optimization problems in these chapters are NP-hard and provide the corresponding mixed integer linear programming models. Moreover, we design polynomial-time heuristic algorithms to solve them approximately. In each chapter, we run experiments on multiple synthetic and real networks to compare the performance of the mixed integer linear programming models and the heuristic algorithms. The results show that the heuristic algorithms show their efficacy and efficiency compared to the mixed-integer linear programming models

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin