Modelling socio-ecological systems: Implementation of an advanced Fuzzy Cognitive Map framework for policy development for addressing complex real-life challenges

This study implements a novel Fuzzy Cognitive Map (FCM) framework for addressing large complex socio-ecological problems. These are characterized as qualitative, dominated by uncertainty, human involvement with different and vague perceptions/expectations, and complex systems dynamics due to feedback relations. The FCM framework provides a participatory soft computing approach to develop consensus solutions. We demonstrate its implementation in a case study: a national-scale acute water scarcity crisis. The model has eight steps starting from collecting data from stakeholders in the form of FCMs (bi-directional graphs) represented by nodes and imprecise connections. All subsequent steps operate within a new fuzzy 2-tuple framework that overcomes previous FCM limitations through advanced processing methods, where large FCMs are fuzzified and analyzed, condensed, and aggregated using graph-theoretic measures. FCMs are simulated as Auto-Associative Neural Networks (AANN) to assess policy solutions to address the problem. In this study, very large cognitive maps were developed through interviews capturing perceptions of five different stakeholder groups taking into consideration the causes, consequences and challenges of the acute water scarcity problem in Jordan. The complex FCMs containing 186 variables comprehensively covered all aspects of water scarcity. FCMs were condensed into smaller maps in two levels. They were also combined into five stakeholder group FCMs and one whole system FCM (total 123 FCMs). AANN simulations of policy scenarios were conducted on the whole system FCM, first at the most condensed level and then moved top-down through the next two levels of granularity to explore potential solutions. These were ranked by a novel fuzzy Appropriateness criterion to provide a number of high level and effective strategies to mitigate the water crisis

A hierarchical systems modelling approach based on neural networks for forecasting global waste generation: a case study of Chile

In this-first every study for Chile, a neural network based hierarchical modelling approach is proposed for forecasting domestic waste generation for the whole country. Over 30 global variables from the 342 communes (municipalities) in the country were analysed extensively using statistical tools that led to 5 significant explanatory variables: population, percentage of urban population, years of education, number of libraries and number of indigents. The five explanatory variables were used to develop a feedforward neural network for predicting volume of global waste generation for a particular year (2002 in this case) in Chile and assessing the contribution of variables. The model had validation R² of 0.82

A comprehensive conceptual and computational dynamics framework for autonomous regeneration of form and function in biological organisms

In biology, regeneration is a mysterious phenomenon that has inspired self-repairing systems, robots, and biobots. It is a collective computational process whereby cells communicate to achieve an anatomical set point and restore original function in regenerated tissue or the whole organism. Despite decades of research, the mechanisms involved in this process are still poorly understood. Likewise, the current algorithms are insufficient to overcome this knowledge barrier and enable advances in regenerative medicine, synthetic biology, and living machines/biobots. We propose a comprehensive conceptual framework for the engine of regeneration with hypotheses for the mechanisms and algorithms of stem cell-mediated regeneration that enables a system like the planarian flatworm to fully restore anatomical (form) and bioelectric (function) homeostasis from any small- or large-scale damage. The framework extends the available regeneration knowledge with novel hypotheses to propose collective intelligent self-repair machines, with multi-level feedback neural control systems, driven by somatic and stem cells. We computationally implemented the framework to demonstrate the robust recovery of both anatomical and bioelectric homeostasis in an worm that, in a simple way, resembles the planarian. In the absence of complete regeneration knowledge, the framework contributes to understanding and generating hypotheses for stem cell mediated form and function regeneration which may help advance regenerative medicine and synthetic biology. Further, as our framework is a bio-inspired and bio-computing self-repair machine, it may be useful for building self-repair robots/biobots and artificial self-repair systems

Using emergent clustering methods to analyse short time series gene expression data from childhood leukemia treated with glucocorticoids

Acute lymphoblastic leukemia (ALL) causes the highest number of deaths from cancer in children aged between one and fourteen. The most common treatment for children with ALL is chemotherapy, a cancer treatment that uses drugs to kill cancer cells or stop cell division. The drug and dosage combinations may vary for each child. Unfortunately, chemotherapy treatments may cause serious side effects. Glucocorticoids (GCs) have been used as therapeutic agents for children with ALL for more than 50 years. Common and widely drugs in this class include prednisolone and dexamethasone. Childhood leukemia now has a survival rate of 80% (Pui, Robison, & Look, 2008). The key clinical question is identifying those children who will not respond well to established therapy strategies.GCs regulate diverse biological processes, for example, metabolism, development, differentiation, cell survival and immunity. GCs induce apoptosis and G1 cell cycle arrest in lymphoid cells. In fact, not much is known about the molecular mechanism of GCs sensitivity and resistance, and GCs-induced apoptotic signal transduction pathways and there are many controversial hypotheses about both genes regulated by GCs and potential molecular mechanism of GCs-induced apoptosis. Therefore, understanding the mechanism of this drug should lead to better prognostic factors (treatment response), more targeted therapies and prevention of side effects. GCs induced apoptosis have been studied by using microarray technology in vivo and in vitro on samples consisting of GCs treated ALL cell lines, mouse thymocytes and/or ALL patients. However, time series GCs treated childhood ALL datasets are currently extremely limited. DNA microarrays are essential tools for analysis of expression of many genes simultaneously. Gene expression data show the level of activity of several genes under experimental conditions. Genes with similar expression patterns could belong to the same pathway or have similar function. DNA microarray data analysis has been carried out using statistical analysis as well as machine learning and data mining approaches. There are many microarray analysis tools; this study aims to combine emergent clustering methods to get meaningful biological insights into mechanisms underlying GCs induced apoptosis. In this study, microarray data originated from prednisolone (glucocorticoids) treated childhood ALL samples (Schmidt et al., 2006) (B-linage and T-linage) and collected at 6 and 24 hours after treatment are analysed using four methods: Selforganizing maps (SOMs), Emergent self-organizing maps (ESOM) (Ultsch & Morchen, 2005), the Short Time series Expression Miner (STEM) (Ernst & Bar-Joseph, 2006) and Fuzzy clustering by Local Approximation of MEmbership (FLAME) (Fu & Medico, 2007). The results revealed intrinsic biological patterns underlying the GCs time series data: there are at least five different gene activities happening during the three time points; GCs-induced apoptotic genes were identified; and genes active at both time points or only at 6 hours or 24 hours were determined. Also, interesting gene clusters with membership in already known pathways were found thereby providing promising candidate gens for further inferring GCs induced apoptotic gene regulatory networks

CDK2 and CKI targeting can significantly lower the cellular senescence bar - reveals a mathematical model of G1/S checkpoint pathway

Cellular senescence, a mechanism employed by cells for thwarting proliferation, has shown to play an important role in protecting cells against cancer development in recent experimental observations, indicating that a deeper understanding of the cellular senescence pathway can help exploit its capacity for more effective cancer treatment. Furthermore, some experimental evidence points out that inhibition of CDK2 or Skp2 can be the critical trigger for cellular senescence. However, no mathematical model has been developed to highlight cellular senescence until now. In this study, we first implement a mathematical model of G1/S transition involving the DNA-damage pathway to highlight cellular senescence by lowering the critical trigger- CDK2. For this, we focus on the behaviour of two important proteins (E2F and CycE) for several reduced CDK2 levels under two DNA-damage conditions by calculating the probability (β) of DNA-damaged cells passing the G1/S. Our recently published results from the same model indicated that a large percentage of damaged cells pass G1/S under normal CDK2 levels, reaching β values of up to 65% under high level of DNA damage. The current study reveals that reduced CDK2 levels can significantly lower the percentage of damaged cells passing the G1/S; in particular, 50% reduction in CDK2 achieves 65% reduction in the passage of damaged cells. Furthermore, the model analyses the relationship between CDK2 and its CKIs in search of other effective ways to bring forward cellular senescence. Results show that the degradation rate of p21 and initial concentration of p27 can be effectively used to lower the senescence threshold. Specifically, p27 is the most effective, followed by CDK2 and p21. However, the combined effect of CDK2 and CKIs is dramatic with CDK2/p27 combination almost totally arresting the passage of damaged cells. Biologists may wish to validate the efficacy of these targets for treating cancer

A comprehensive conceptual and computational dynamics framework for autonomous regeneration systems

This paper presents a new conceptual and computational dynamics framework for damage detection and regeneration in multicellular structures similar to living animals. The model uniquely achieves complete and accurate regeneration from any damage anywhere in the system. We demonstrated the efficacy of the proposed framework on an artificial organism consisting of three tissue structures corresponding to the head, body and tail of a worm. Each structure consists of a stem cell surrounded by a tissue of differentiated cells. We represent a tissue as an Auto-Associative Neural Network (AANN) with local interactions and stem cells as a self-repair network with long-range interactions. We also propose another new concept, Information Field which is a mathematical abstraction over traditional components of tissues, to keep minimum pattern information of the tissue structures to be accessed by stem cells in extreme cases of damage. Through entropy, a measure of communication between a stem cell and differentiated cells, stem cells monitor the tissue pattern integrity, violation of which triggers damage detection and tissue repair. Stem cell network monitors its state and invokes stem cell repair in the case of stem cell damage. The model accomplishes regeneration at two levels: In the first level, damaged tissues with intact stem cells regenerate themselves. Here, stem cell identifies entropy change and finds the damage and regenerates the tissue in collaboration with the AANN. In the second level, involving missing whole tissues and stem cells, the remaining stem cell(s) access the information field to restore the stem cell network and regenerate missing tissues. In the case of partial tissue damage with missing stem cells, the two levels collaborate to accurately restore the stem cell network and tissues. This comprehensive hypothetical framework offers a new way to conceptualise regeneration for better understanding the regeneration processes in living systems. It could also be useful in biology for regenerative medicine and in engineering for building self-repairing biobots

Resilience models for New Zealand's alpine skiers based on people's knowledge and experience: a mixed method and multi-step fuzzy cognitive mapping approach

Artificial Neural Networks (ANN) as a tool offers opportunities for modeling the inherent complexity and uncertainty associated with socio-environmental systems. This study draws on New Zealand ski fields (multiple locations) as socio- environmental systems while considering their perceived resilience to low probability but potential high consequences catastrophic natural events (specifically earthquakes). We gathered data at several ski fields using a mixed methodology including: geomorphic assessment, qualitative interviews, and an adaptation of Ozesmi and Ozesmi’s (2003) multi-step fuzzy cognitive mapping (FCM) approach. The data gathered from FCM are qualitatively condensed, and aggregated to three different participant social groups. The social groups include ski fields users, ski industry workers, and ski field managers. Both quantitative and qualitative indices are used to analyze social cognitive maps to identify critical nodes for ANN simulations. The simulations experiment with auto-associative neural networks for developing adaptive preparation, response and recovery strategies. Moreover, simulations attempt to identify key priorities for preparation, response, and recovery for improving resilience to earthquakes in these complex and dynamic environments. The novel mixed methodology is presented as a means of linking physical and social sciences in high complexity, high uncertainty socio-environmental systems. Simulation results indicate that participants perceived that increases in Social Preparation Action, Social Preparation Resources, Social Response Action and Social Response Resources have a positive benefit in improving the resilience to earthquakes of ski fields’ stakeholders

Creep Modeling of Wood Using Time-Temperature Superposition

The time-temperature superposition principle was used to develop long-term compression creep and recovery models for southern pine exposed to constant environmental conditions using shortterm data. Creep (17-hour) and recovery (40-hour) data were obtained at constant temperature levels ranging from 70 F to 150 F and constant equilibrium moisture content (EMC) of 9%. The data were plotted against log-time, and the resultant curve segments were shifted along the log-time axis with respect to the curve for ambient conditions to construct a master curve applicable to ambient conditions (70 F, 9% EMC) and a longer time period. The master curves were represented by power functions, and they predicted up to 6.4 years of creep and 5.8 years of recovery response. The validity of the master curves for predicting creep of wood exposed to the normal interior environment in buildings was tested by conducting ten-month creep tests in the laboratory. The fluctuating environment caused geometry changes in the surface of the specimens affecting the collected long-term data. Therefore, a good comparison between the master curves and the long-term data was not possible

The use of artificial neural networks to diagnose mastitis in dairy cattle

The use of milk sample categorization for diagnosing mastitis using Kohonen's self-organizing feature map (SOFM) is reported. Milk trait data of 14 weeks of milking from commercial dairy cows in New Zealand was used to train and test a SOFM network. The SOFM network was useful in discriminating data patterns into four separate mastitis categories. Several other artificial neural networks were tested to predict the missing data from the recorded milk traits. A multi-layer perceptron (MLP) network proved to be most accurate (R² = 0.84, r = 0.92) when compared to other MLP (R² = 0.83, r = 0.92), Elman (R² = 0.80, r = 0.92), Jordan (R² = 0.81, r = 0.92) or linear regression (R² = 0.72, r = 0.85) methods. It is concluded that the SOFM can be used as a decision tool for the dairy farmer to reduce the incidence of mastitis in the dairy herd

Methodology for development of single cell dendritic spine (SCDS) synaptic tagging and capture model using Virtual Cell (VCell)

Single cell dendritic spine modelling methodology has been adopted to explain structural plasticity and respective change in the neuronal volume previously. However, the single cell dendrite methodology has not been employed previously to explain one of the important aspects of memory allocation i.e., Synaptic tagging and Capture (STC) hypothesis. It is difficult to relate the physical properties of STC pathways to structural changes and synaptic strength. We create a mathematical model based on earlier reported synaptic tagging networks. We built the model using Virtual Cell (VCell) software and used it to interpret experimental data and investigate the behavior and characteristics of known Synaptic tagging candidates. • We investigate processes associated with synaptic tagging candidates and compare them to the assumptions based on the STC hypothesis. • We assess the behavior of several reported synaptic tagging candidates against the requirements outlined in the synaptic tagging hypothesis