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With the aim to identify new fault diagnosis and advanced robotic systems, this paper first proposes a fault diagnosis algorithm based on an artificial immune network model that can adjust the pruning threshold. Secondly, the algorithm is improved based on neighbourhood rough set theory, in which the relationships among the pruning threshold, misdiagnosis rate, and missed diagnosis rate in the shape space are discussed. In addition, an improved algorithm for adjusting the adaptively pruning threshold based solely on an observation index is described. The simulation experiments show that the algorithm can identify the new fault modes while keeping the misdiagnosis and missed diagnosis rates low. [ABSTRACT FROM AUTHOR]

The customary international law (CIL) norm of personal immunity for Heads of State has come under significant fire in the past decade. While immunity norms have traditionally been absolute, the increasing influence of the human rights and anti-impunity movements, coupled with pleas for international criminal responsibility for egregious human rights and humanitarian violations, have eroded them, particularly within international jurisdictions. These changes reflect a larger challenge to the traditional statecentric model. Although states remain the primary makers of international law, many other participants, including international organizations, courts, and non-governmental organizations (NGOs), are crucial to the development of international legal norms today. But there is, of yet, no formal model integrating these actors into existing legal frameworks. The goal of this Article is to provide an analytical framework to apply to the shifting norm of personal immunity for Heads of State based on the relationships and connections among actors. Using the tools of network theory, this Article determines the defining properties of this network of actors, including its topology, density, centrality, and actor similarity, which explain current normative shifts and predict developments. Based on this quantitative analysis, this Article puts forward two arguments. First, non-state actors, even though not formally accepted as capable of contributing to international law, have a clear normative effect. Second, insofar as the hubs in this network continue to pursue an exception to Head of State immunity before International Criminal Courts, we are likely to see an exception crystallize as a new rule of CIL. Viewing international law through networks of actors provides lawyers and policy-makers with a descriptive tool that translates and maps the elusive global realities that lead to international law-making. [ABSTRACT FROM AUTHOR]

The customary international law (CIL) norm of personal immunity for Heads of State has come under significant fire in the past decade. While immunity norms have traditionally been absolute, the increasing influence of the human rights and anti-impunity movements, coupled with pleas for international criminal responsibility for egregious human rights and humanitarian violations, have eroded them, particularly within international jurisdictions. These changes reflect a larger challenge to the traditional statecentric model. Although states remain the primary makers of international law, many other participants, including international organizations, courts, and non-governmental organizations (NGOs), are crucial to the development of international legal norms today. But there is, of yet, no formal model integrating these actors into existing legal frameworks. The goal of this Article is to provide an analytical framework to apply to the shifting norm of personal immunity for Heads of State based on the relationships and connections among actors. Using the tools of network theory, this Article determines the defining properties of this network of actors, including its topology, density, centrality, and actor similarity, which explain current normative shifts and predict developments. Based on this quantitative analysis, this Article puts forward two arguments. First, non-state actors, even though not formally accepted as capable of contributing to international law, have a clear normative effect. Second, insofar as the hubs in this network continue to pursue an exception to Head of State immunity before International Criminal Courts, we are likely to see an exception crystallize as a new rule of CIL. Viewing international law through networks of actors provides lawyers and policy-makers with a descriptive tool that translates and maps the elusive global realities that lead to international law-making. [ABSTRACT FROM AUTHOR]

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Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once--for example, communication within a group rather than person to person, collaboration among a team rather than a pair of coauthors, or biological interaction between a set of molecules rather than just two. Such higher-order interactions are ubiquitous, but their empirical study has received limited attention, and little is known about possible organizational principles of such structures. Here we study the temporal evolution of 19 datasets with explicit accounting for higher-order interactions. We show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higher-order structure. Furthermore, we find that tie strength and edge density are competing positive indicators of higher-order organization, and these trends are consistent across interactions involving differing numbers of nodes. To systematically further the study of theories for such higher-order structures, we propose higher-order link prediction as a benchmark problem to assess models and algorithms that predict higher-order structure. We find a fundamental difference from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions. [ABSTRACT FROM AUTHOR]

Dual-phase evolution (DPE) and the network theory help to analyze prominent properties of the complex adaptive systems (CASs) such as emergence and self-organization that are caused due to the phase transitions. These transitions are observed because of the increase and decrease in the number of system components and their interactions. The immune system, which is one of the CASs, provides an adaptive response to the foreign molecules. Prior to this response, the immune system is present in the circulation state and during the response, it moves into the growth state, where the number of immune cells and their cell–cell contacts increase rapidly. The phase transitions from the circulation state to the growth state and then back to the circulation state cause the emergence and self-organization of the immune system, respectively. There is a need to understand these complex cellular dynamics during the immune response. In this paper, we have proposed an integrated model of DPE, network theory, and the immune system that has helped to understand and analyze the phases and properties of the immune system. Analysis of the growth phase network is provided and it is concluded that this network exhibits scale-free nature following power law for the degree distribution of nodes. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the effects of Hypericum perforatum extract on the endocrine immune network factors in the immunosuppressed rat. Wistar rats were used to establish an immunosuppression model. Immunosuppressed rats received Hypericum perforatum extract suspension for 7 days at doses of 600 mg/kg (containing hyperforin at 7.2mg/kg).Endocrine immune network factors including IL-1, DA, NE, and 5-HT were determined on day 8.The results indicated that Hypericum perforatum extract affects the endocrine immune network through decreasing endogenous IL-1,dopamine (DA), norepinephrine (NE), and 5- hydroxytryptamine (5-HT) levels. Hyperforin reverses immune suppression through enhancing DA levels to inhibit B cells while promoting T cell and macrophage functions to recover immune function. [ABSTRACT FROM AUTHOR]

A detailed understanding of the fine specificity of serotype-specific human antibodies is vital for the development and evaluation of new vaccines for pathogenic flaviviruses such as dengue virus (DENV) and Zika virus. In this study, we thoroughly characterize the structural footprint of an anti-idiotype antibody (E1) specific for a potent, fully human DENV serotype 1-specific antibody, termed HM14c10, derived from a recovered patient. The crystal structure at a resolution of 2.5 Å of a complex between the Fab fragments of E1 and HM14c10 provides the first detailed molecular comparison of an anti-idiotype paratope specific for a human antibody with its analogous epitope, a discontinuous quaternary structure located at the surface of the viral particle that spans adjacent envelope (E) proteins. This comparison reveals that the footprints left by E1 and E on HM14c10 largely overlap, explaining why the formation of binary complexes is mutually exclusive. Structural mimicry of the DENV E epitope by the E1 combining site is achieved via the formation of numerous interactions with heavy chain complementarity domain regions (CDRs) of HM14c10, while fewer interactions are observed with its light chain than for the E protein. We show that E1 can be utilized to detect HM14c10-like antibodies in sera from patients who recovered from DENV-1, infection suggesting that this is a public (common) idiotype. These data demonstrate the utility of employing an anti-idiotype antibody to monitor a patient's specific immune responses and suggest routes for the improvement of E "mimicry" by E1 by increasing its recognition of the Fab HM14c10 light chain CDRs. IMPORTANCE A chimeric yellow fever-dengue live-attenuated tetravalent vaccine is now being marketed. Dengue remains a significant public health problem, because protection conferred by this vaccine against the four circulating serotypes is uneven. Reliable tools must be developed to measure the immune responses of individuals exposed to DENV either via viral infection or through vaccination. Anti-idiotypic antibodies provide precision tools for analyzing the pharmacokinetics of antibodies in an immune response and also for measuring the amount of circulating anti-infective therapeutic antibodies. Here, we characterize how an anti-idiotypic antibody (E1) binds antibody HM14c10, which potently neutralizes DENV serotype 1. We report the crystal structure at a resolution of 2.5 Å of a complex between the Fab fragments of E1 and HM14c10 and provide the first detailed molecular comparison between the anti-idiotype surface and its analogous epitope located at the surface of the dengue virus particle. [ABSTRACT FROM AUTHOR]

We consider self-tolerance and its failure—autoimmunity—in a minimal mathematical model of the idiotypic network. A node in the network represents a clone of B-lymphocytes and its antibodies of the same idiotype which is encoded by a bitstring. The links between nodes represent possible interactions between clones of almost complementary idiotype. A clone survives only if the number of populated neighbored nodes is neither too small nor too large. The dynamics is driven by the influx of lymphocytes with randomly generated idiotype from the bone marrow. Previous work has revealed that the network evolves toward a highly organized modular architecture, characterized by groups of nodes which share statistical properties. The structural properties of the architecture can be described analytically, the statistical properties determined from simulations are confirmed by a modular mean-field theory. To model the presence of self we permanently occupy one or several nodes. These nodes influence their linked neighbors, the autoreactive clones, but are themselves not affected by idiotypic interactions. The architecture is very similar to the case without self, but organized such that the neighbors of self are only weakly occupied, thus providing self-tolerance. This supports the perspective that self-reactive clones, which regularly occur in healthy organisms, are controlled by anti-idiotypic clones. We discuss how perturbations, like an infection with foreign antigen, a change in the influx of new idiotypes, or the random removal of idiotypes, may lead to autoreactivity and devise protocols which cause a reconstitution of the self-tolerant state. The results could be helpful to understand network and probabilistic aspects of autoimmune disorders. [ABSTRACT FROM AUTHOR]

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology. [ABSTRACT FROM AUTHOR]

The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties. Lastly, we perform a procedure for in-silico simulation of a treatment action in a biological pathway. The findings obtained by applying the developed pipeline, including gene expression analysis, confirms how a network-based bioinformatics method is able to prioritize candidate genes for in vitro analysis, thus speeding up the understanding of molecular mechanisms and supporting the discovery of potential drug targets. [ABSTRACT FROM AUTHOR]

Bistability has important implications in signaling pathways, since it indicates a potential cell decision between alternative outcomes. We present two approaches developed in the framework of the Chemical Reaction Network Theory for easy and efficient search of multiple steady state behavior in signaling networks (both with and without mass conservation), and apply them to search for sources of bistability at different levels of the interferon signaling pathway. Different type I interferon subtypes and/or doses are known to elicit differential bioactivities (ranging from antiviral, antiproliferative to immunomodulatory activities). How different signaling outcomes can be generated through the same receptor and activating the same JAK/STAT pathway is still an open question. Here, we detect bistability at the level of early STAT signaling, showing how two different cell outcomes are achieved under or above a threshold in ligand dose or ligand-receptor affinity. This finding could contribute to explain the differential signaling (antiviral vs apoptotic) depending on interferon dose and subtype (α vs β) observed in type I interferons. [ABSTRACT FROM AUTHOR]

Background: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. Results: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. Conclusion: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions. [ABSTRACT FROM AUTHOR]

Immune memories of antigens are formed as limit behavior of antibody dynamics, which is a simplified model of cyclic idiotypic immune networks. Rapid responses of immune systems to secondary antigens are due to the memory recalls of the stored antigenic attributes. The immune-memory mechanism is studied by combining network structure and dynamical systems. Moreover, associative memory can be explored by network dynamics of a cross-correlation matrix determined by an antibody chain. Analysis of cross-reactive immune responses can be accomplished by introducing the overlapping difference function ρ of network dynamics. Classification of four types of memory recalls was performed here. It explains the phenomena of associative memory and provides methods for control of vaccinations via tuning of suitable parameters. Other issues with immune memory and cyclic idiotypic immune networks are also discussed. [ABSTRACT FROM AUTHOR]

Pattern recognition receptors detect microbial products and induce cytokines, which shape the immunological response. IL-12, TNF-α, and IL-1β are proinflammatory cytokines, which are essential for resistance against infection, but when produced at high levels they may contribute to immunopathology. In contrast, IL-10 is an immunosuppressive cytokine, which dampens proinflammatory responses, but it can also lead to defective pathogen clearance. The regulation of these cytokines is therefore central to the generation of an effective but balanced immune response. In this study, we show that macrophages derived from C57BL/6 mice produce low levels of IL-12, TNF-α, and IL-1β, but high levels of IL-10, in response to TLR4 and TLR2 ligands LPS and Pam3CSK4, as well as Burkholderia pseudomallei, a Gram-negative bacterium that activates TLR2/4. In contrast, macrophages derived from BALB/c mice show a reciprocal pattern of cytokine production. Differential production of IL-10 in B. pseudomallei and LPS-stimulated C57BL/6 and BALB/c macrophages was due to a type I IFN and ERK1/2-dependent, but IL-27–independent, mechanism. Enhanced type I IFN expression in LPS-stimulated C57BL/6 macrophages was accompanied by increased STAT1 and IFN regulatory factor 3 activation. Furthermore, type I IFN contributed to differential IL-1β and IL-12 production in B. pseudomallei and LPS-stimulated C57BL/6 and BALB/c macrophages via both IL-10–dependent and –independent mechanisms. These findings highlight key pathways responsible for the regulation of pro- and anti-inflammatory cytokines in macrophages and reveal how they may differ according to the genetic background of the host. [ABSTRACT FROM AUTHOR]

Arthrographis kalrae is occasionally described as an opportunistic human pathogen. This study investigated the immune response to A. kalrae during murine experimental infection (7, 14, 28 and 56 days post infection). The fungal load was higher in the early phase and mice presented with neurological syndrome over the course of the infection. There was a gradual increase in the level of anti- A. kalrae IgG and increased levels of DTH at 14 days. There was decreased IFN-γ (14–56 days) and an increase in IL-4 (7 and 56 days). Decreased levels of cytokines (IFN-γ, IL-4, IL-10 and IL-17) were observed in the brain at 56 days p.i. The results suggest that the immune response during murine A. kalrae infection modulates to the pattern of Th2 response. This study shows for the first time the cytokines and cellular immunomodulation that occur in response to an experimental infection with A. kalrae in mice. [ABSTRACT FROM AUTHOR]

Artificial immune systems (AIS) have been widely utilized for pattern recognition and data analysis in various fields of science and technology, and artificial immune networks (AIN) are based on AIS. In this study, an artificial immune network is used for collaborative filtering as a classification model for bank term deposit recommendations, once feature selection has been applied to filter out key features for classification purposes. AIN is used to represent a network of customers with bank term deposits, and it can be adopted as a group decision-making model in predicting whether a new customer will have a term deposit or not. Formulae for calculating the affinity between an antigen and an antibody, and the affinity of an antigen to an immune network are also developed. A series of experiments are conducted, and the results are very encouraging. Despite the class imbalance problem in the test dataset, the proposed model outperformed other models, achieving the highest accuracy in testing. [ABSTRACT FROM AUTHOR]

A challenge in neuroscience is to integrate the cellular and system levels. For instance, we still do not know how a few dozen neurons organize their activity and relations in a microcircuit or module of histological scale. By using network theory and Ca 2+ imaging with single-neuron resolution we studied the way in which striatal microcircuits of dozens of cells orchestrate their activity. In addition, control and diseased striatal tissues were compared in rats. In the control tissue, functional connectomics revealed small-world, scale-free and hierarchical network properties. These properties were lost during pathological conditions in ways that could be quantitatively analyzed. Decorticated striatal circuits disclosed that corticostriatal interactions depend on privileged connections with a set of highly connected neurons or “hubs”. In the 6-OHDA model of Parkinson's disease there was a decrease in hubs number; but the ones that remained were linked to dominant network states. l -DOPA induced dyskinesia provoked a loss in the hierarchical structure of the circuit. All these conditions conferred distinct temporal sequences to circuit activity. Temporal sequences appeared as particular signatures of disease process thus bringing the possibility of a future quantitative pathophysiology at a histological scale. [ABSTRACT FROM AUTHOR]

Alterations in neural networks are associated with the cognitive difficulties of the prematurely born. Using functional magnetic resonance imaging, we analyzed functional connectivity for preterm (PT) and term neonates at term equivalent age. Specifically, we constructed whole-brain networks and examined rich club (RC) organization, a common construct among complex systems where important (or 'rich') nodes connect preferentially to other important nodes. Both PT and term neonates showed RC organization with PT neonates exhibiting significantly reduced connections between these RC nodes. Additionally, PT neonates showed evidence of weaker functional segregation. Our results suggest that PT birth is associated with fundamental changes of functional organization in the developing brain. [ABSTRACT FROM AUTHOR]

In this review, we summarize and discuss some key findings from the study of naturally occurring autoantibodies. The B-cell compartment of the immune system appears to recognize almost all endogenous and environmental antigens. This ability is accomplished principally through autopolyreactive humoral and cellular immune receptors. This extended autopolyreactivity (1) along immunoglobulin gene recombination contributes to the immune system's ability to recognize a very large number of self and non-self constituents; and (2) generates a vast immune network that creates communication channels between the organism's interior and exterior. Thus, the immune system continuously evolves depending on the internal and external stimuli it encounters. Furthermore, this far-reaching network's existence implies activities resembling those of classical biological factors or activities that modulate the function of other classical biological factors. A few such antibodies have already been found. Another important concept is that natural autoantibodies are highly dependent on the presence or absence of commensal microbes in the organism. These results are in line with past and recent findings showing the fundamental influence of the microbiota on proper immune system development, and necessitate the existence of a host -microbe homeostasis. This homeostasis requires that the participating humoral and cellular receptors are able to recognize self-antigens and commensal microbes without damaging them. Autopolyreactive immune receptors expressing low affinity for both types of antigens fulfil this role. The immune system appears to play a holistic role similar to that of the nervous system. [ABSTRACT FROM AUTHOR]

Tracer kinetic modeling (TKM) is a promising quantitative method for physiological and biochemical processes in vivo. In this paper, we investigated the applications of an immune-inspired method to better address the issues of Simultaneous Estimation (SIME) of TKM with multimodal optimization. Experiments of dynamic FDG PET imaging experiments and simulation studies were carried out. The proposed artificial immune network (TKM_AIN) shows more scalable and effective when compared with the gradient-based Marquardt–Levenberg algorithm and the scholastic-based simulated annealing method. [ABSTRACT FROM AUTHOR]

The ability of artificial immune systems to adapt to varying pathogens makes such systems a suitable choice for various robotic applications. Generally, immunity-based robotic applications map local instantaneous sensory information into either an antigen or a co-stimulatory signal, according to the choice of representation schema. Algorithms then use relevant immune functions to output either evolved antibodies or maturity of dendritic cells, in terms of actuation signals. It is observed that researchers do not try to replicate the biological immunity but select necessary immune functions instead, resulting in an ad-hoc manner these applications are reported. On the other hand, the paradigm shift in robotics research from reactive to probabilistic approaches is also not being reflected in these applications. Authors, therefore, present a detailed review of immuno-inspired robotic applications in an attempt to identify the possible areas to explore. Moreover, the literature has been categorized according to the underlying immuno-definitions. Implementation details have been critically reviewed in terms of corresponding mathematical expressions and their representation schema that include binary, real or hybrid approaches. Limitations of reported applications have also been identified in light of modern immunological interpretations including the danger theory. As a result of this study, authors suggest a renewed focus on innate immunity, action contextualization prior to B/T cell invocation and behavior evolution instead of arbitration. In this context, a multi-tier immunological framework for robotics research, combining innate and adaptive components together is also suggested and skeletonized. [ABSTRACT FROM AUTHOR]

Theories are indispensable to organize immunological data into coherent, explanatory, and predictive frameworks. We propose to combine different models to develop a unifying theory of immunity which situates immunology in the wider context of physiology. We believe that the immune system will be increasingly understood as a central component of a network of partner physiological systems that interconnect to maintain homeostasis. [ABSTRACT FROM AUTHOR]

Head and neck squamous cell carcinoma (HNSCC) is the most common form of head and neck cancer. Annually, more than half a million individuals are diagnosed with this devastating disease, with increasing incidence in Europe and Southeast Asia. The diagnosis of HNSCC often occurs in late stages of the disease and is characterized by manifestation of a high-grade primary tumor and/or lymph node metastasis, precluding timely management of this deadly cancer. Recently, HNSCC cancer stem cells have emerged as an important factor for cancer initiation and maintenance of tumor bulk. Like normal stem cells, cancer stem cells can undergo self-renewal and differentiation. This unique trait allows for maintenance of the cancer stem cell pool and facilitates differentiation into heterogeneous neoplastic progeny when necessary. Recent studies have suggested coexistence of different cancer stem cell populations within a tumor mass, where the tumor initiation and metastasis properties of these cancer stem cells can be uncoupled. Cancer stem cells also possess resistant phenotypes that evade standard chemotherapy and radiotherapy, resulting in tumor relapse. Therefore, understanding distinctive pathways relating to cancer stem cells will provide insight into early diagnosis and treatment of HNSCC. In this review, we highlight current advances in identifying cancer stem cells, detail the interactions of these cells with the immune system within the tumor niche, and discuss the potential use of immunotherapy in managing HNSCC. [ABSTRACT FROM AUTHOR]

Utilities typically depend on customers to call and inform them of the service interruptions due to outages by entering their addresses. After sufficient calls are received, the utility is able to pinpoint the location of outages. It is essential for the utility to have a good idea on the location of outages before sending crew for restoration work to optimally utilize their resources. This article uses an idiotypic immune system to model and simulate radial distribution system to identify the operated protective devices due to outages. The incoming calls are modeled as antigens,and the protective devices are modeled as antibodies. Using a differential equation describing dynamics of the immune system, the operated protective devices in the network are identified. A pre-processing technique is presented to handle cases with multiple outages within the same period. Different threshold values for call grouping are tested to determine the most suitable values. Several real-life examples are considered to demonstrate that the proposed method is able to identify the correct protective devices. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology for deriving a model of a complex system by exploiting the information extracted from topological data analysis. Central to our approach is the S[B] paradigm in which a complex system is represented by a two-level model. One level, the structural S one, is derived using the newly-introduced quantitative concept of persistent entropy, and it is described by a persistent entropy automaton. The other level, the behavioral B one, is characterized by a network of interacting computational agents. The presented methodology is applied to a real case study, the idiotypic network of the mammalian immune system. [ABSTRACT FROM AUTHOR]

Self-directed lymphocytes may evade clonal deletion at ontogenesis but still remain harmless due to a mechanism called clonal anergy. For B-lymphocytes, two major explanations for anergy developed over the last decades: according to Varela theory, anergy stems from a proper orchestration of the whole B-repertoire, such that self-reactive clones, due to intensive feed-back from other clones, display strong inertia when mounting a response. Conversely, according to the model of cognate response, self-reacting cells are not stimulated by helper lymphocytes and the absence of such signaling yields anergy. Through statistical mechanics we show that helpers do not prompt activation of a sub-group of B-cells: remarkably, the latter are just those broadly interacting in the idiotypic network. Hence Varela theory can finally be reabsorbed into the prevailing framework of the cognate response model. Further, we show how the B-repertoire architecture may emerge, where highly connected clones are self-directed as a natural consequence of ontogenetic learning. [ABSTRACT FROM AUTHOR]

The regulation of autoimmunity is a key issue in fundamental immunology. Despite outstanding achievements on this front, we currently have more questions than answers. The idea of an immune network as a regulatory mechanism is quite attractive, since it enables us to explain the selectivity (specificity), and moreover the clonality, of the regulation. Nevertheless it remains unclear how this mysterious network of immune cells is organized, how it operates, and how it exerts control over autoimmunity. This article presents an attempt to understand how the immune network functions and how it controls autoreactivity. We present a mathematical model of the immune network that is based on principles of immune network organization and function that we arrived at from a survey of the available literature. To test the principles on which the mathematical model is based, we studied the model and compared the different responses to antigen that it generated with the results obtained from experimental studies of immune response. The modeled kinetics of idiotype and anti-idiotype in response to the administration of antigen are in good agreement with the experimental kinetics of idiotypic and anti-idiotypic antibodies. To obtain evidence of the existence of idiotypic mechanisms for regulating autoimmunity, we studied a mathematical model containing autoclones and compared the model results with data from experimental studies in a model of autoimmune hemolytic anemia in mice. Because the results from the theoretical and the experimental studies coincide, there is justification to conclude that autoreactive lymphocytes are normal components of the immune network within which they are regulated. We discuss a possible molecular/cellular mechanism for negative control of autoreactive cells as affected by anti-idiotypic antibodies. [ABSTRACT FROM AUTHOR]

The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox is a fully automated all-in-one connectivity analysis toolbox that offers both pre-processing, connectivity, and graph theory analysis of multimodal images such as anatomical, diffusion, and functional MRI, and PET. In this work, the MIBCA functionalities were used to study Alzheimer’s Disease (AD) in a multimodal MR/PET approach. Materials and Methods: Data from 12 healthy controls, and 36 patients with EMCI, LMCI and AD (12 patients for each group) were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), including T1-weighted (T1-w), Diffusion Tensor Imaging (DTI) data, and 18F-AV-45 (florbetapir) dynamic PET data from 40-60 min post injection (4x5 min). Both MR and PET data were automatically pre-processed for all subjects using MIBCA. T1-w data was parcellated into cortical and subcortical regions-of-interest (ROIs), and the corresponding thicknesses and volumes were calculated. DTI data was used to compute structural connectivity matrices based on fibers connecting pairs of ROIs. Lastly, dynamic PET images were summed, and the relative Standard Uptake Values calculated for each ROI. Results: An overall higher uptake of 18F-AV-45, consistent with an increased deposition of beta-amyloid, was observed for the AD group. Additionally, patients showed significant cortical atrophy (thickness and volume) especially in the entorhinal cortex and temporal areas, and a significant increase in Mean Diffusivity (MD) in the hippocampus, amygdala and temporal areas. Furthermore, patients showed a reduction of fiber connectivity with the progression of the disease, especially for intra-hemispherical connections. Conclusion: This work shows the potential of the MIBCA toolbox for the study of AD, as findings were shown to be in agreement with the literature. Here, only structural changes and beta-amyloid accumulation were considered. Yet, MIBCA is further able to process fMRI and different radiotracers, thus leading to integration of functional information, and supporting the research for new multimodal biomarkers for AD and other neurodegenerative diseases. [ABSTRACT FROM PUBLISHER]

The canonical Wnt signaling pathway, mediated by β-catenin, is crucially involved in development, adult stem cell tissue maintenance, and a host of diseases including cancer. We analyze existing mathematical models of Wnt and compare them to a new Wnt signaling model that targets spatial localization; our aim is to distinguish between the models and distill biological insight from them. Using Bayesian methods we infer parameters for each model from mammalian Wnt signaling data and find that all models can fit this time course. We appeal to algebraic methods (concepts from chemical reaction network theory and matroid theory) to analyze the models without recourse to specific parameter values. These approaches provide insight into aspects of Wnt regulation: the new model, via control of shuttling and degradation parameters, permits multiple stable steady states corresponding to stem-like vs. committed cell states in the differentiation hierarchy. Our analysis also identifies groups of variables that should be measured to fully characterize and discriminate between competing models, and thus serves as a guide for performing minimal experiments for model comparison. [ABSTRACT FROM AUTHOR]

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. [ABSTRACT FROM AUTHOR]

This study proposes a modified artificial immune network algorithm for function optimization problems based on idiotypic immune network theory. A hyper-cubic mutation operator was introduced to reduce the heavy computational cost of the traditional opt-AINet algorithm. Moreover, the new symmetrical mutation can effectively improve local search. To maintain population diversity, we also devised an immune selection mechanism based on density and fitness. The global convergence of the algorithm was deduced through the method of pure probability and iterative formula. Simulation results of benchmark function optimization show that the modified algorithm converges more effectively than other immune network algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an agent-based approach using artificial immune system (AIS) paradigms as a successful mechanism for a distributed intrusion detection system (IDS). The AIS paradigms are negative selection, clonal selection, danger theory, and immune network. These paradigms are very successful for anomaly IDS. The AIS paradigms are inspired by the powerful human immune system (HIS) and are promising candidate for design of an IDS. The proposed AIS-based agents are capable of learning, self-adaption, platform mobility, autonomy and collaboration. The proposed system (MAIS-IDS) was designed using these powerful and collaborative agents. This system has mobile and static agents with detector agents as the main actors in MAIS-IDS. The life cycles of agents are determined using the proposed immune algorithms in specific phases. Essential characteristics of MAIS-IDS are cloning, mutation, migration, collaboration, and randomness. MAIS-IDS was evaluated using a network of virtualized hosts, a kernel-based virtual machine (KVM) hypervisor and management Orchestra. [ABSTRACT FROM AUTHOR]

CD1d-restricted natural killer T (NKT) cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II) have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic, and myeloid lineage cells, as well as adaptive populations, especially CD8C and CD4C T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host's ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting. [ABSTRACT FROM AUTHOR]

Highlights: [•] A comprehensive scheduling-based RFID reader anti-collision model is proposed. [•] An artificial immune network with hybrid encoding format is well-designed. [•] The effects of slots, channels, locations and power of readers are investigated. [•] The single-density and multiple-density tag distribution are evaluated. [•] The simulation results suggest the effectiveness and efficiency of AINetHE-RA. [ABSTRACT FROM AUTHOR]

Immune activity in the CNS parenchyma under various acute and chronic neurodegenerative conditions has been often interpreted as a sign of pathological inflammation. The apparent resemblance of the local neuroinflammatory processes to autoimmune diseases, such as multiple sclerosis (MS), generated the view that, despite differences in etiology and pathology, neurodegenerative disorders with a local inflammatory component can benefit from systemic anti-inflammatory therapy. In addition, as CNS self-reactive T cells are associated with the etiology of MS, autoimmunity was assumed to solely reflect pathology, and therefore, was universally linked to autoimmune disease. Yet, it is becoming increasingly clear that CNS-specific T cells, along with circulating and local innate immune cells, can enhance CNS healing processes following non-infectious injuries, or any deviation from homeostasis, including chronic pathological conditions. Here, we discuss the theory of “protective autoimmunity,” which describes the activity of an immune cell network encompassing effector and regulatory T cells with specificity for CNS antigens, in CNS maintenance and repair. Such an immune network, evoked in response to external and internal threats, functions in a tightly regulated way, ensuring restoration of the brain’s equilibrium and return to homeostasis. [ABSTRACT FROM PUBLISHER]

Epidemiclike spreading processes on top of multilayered interconnected complex networks reveal a rich phase diagram of intertwined competition effects. A recent study by the authors [C. Granell et al., Phys. Rev. Lett. Ill, 128701 (2013).] presented an analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the spreading of information awareness to prevent infection, on top of multiplex networks. The results in the case in which awareness implies total immunization to the disease revealed the existence of a metacritical point at which the critical onset of the epidemics starts, depending on completion of the awareness process. Here we present a full analysis of these critical properties in the more general scenario where the awareness spreading does not imply total immunization, and where infection does not imply immediate awareness of it. We find the critical relation between the two competing processes for a wide spectrum of parameters representing the interaction between them. We also analyze the consequences of a massive broadcast of awareness (mass media) on the final outcome of the epidemic incidence. Importantly enough, the mass media make the metacritical point disappear. The results reveal that the main finding, i.e., existence of a metacritical point, is rooted in the competition principle and holds for a large set of scenarios. [ABSTRACT FROM AUTHOR]

We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects. [ABSTRACT FROM AUTHOR]

As a nonspecific physical stimulation, the effect of acupuncture on diseases is produced by motivating the inherent regulatory system in the body, having the characteristics of whole regulation, dual directional regulation, etc. Modern scientific researches show that body’s inherent regulatory system is neuro–endocrine–immune (NEI) network. Hence, we speculate that the regulatory effect of acupuncture may be produced through its regulation of NEI network. In this article, we reviewed the recent researches about acupuncture’s effect on the NEI network, to find out the evidence of acupuncture adjusting NEI network and provide some evidences for revealing the mechanism of acupuncture. [ABSTRACT FROM PUBLISHER]

The recent decline in pollinator biodiversity, notably in the case of wild bee populations, puts both wild and agricultural ecosystems at risk of ecological community collapse. This has triggered calls for further study of these mutualistic communities in order to more effectively inform restoration of disturbed plant-pollinator communities. Here, we use a dynamic network model to test a variety of translocation strategies for restoring a community after it experiences the loss of some of its species. We consider the reintroduction of extirpated species, both immediately after the original loss and after the community has reequilibrated, as well as the introduction of other native species that were originally absent from the community. We find that reintroducing multiple highly interacting generalist species best restores species richness for lightly disturbed communities. However, for communities that experience significant losses in biodiversity, introducing generalist species that are not originally present in the community may most effectively restore species richness, although in these cases the resultant community often shares few species with the original community. We also demonstrate that the translocation of a single species has a minimal impact on both species richness and the frequency of community collapse. These results have important implications for restoration practices in the face of varying degrees of community perturbations, the refinement of which is crucial for community management. [ABSTRACT FROM AUTHOR]