Synchronizing Mind Maps with Fuzzy Cognitive Maps for Decision-Finding in Cognitive Cities

Synchronizing mind maps with fuzzy cognitive maps can help to handle complex problems with many involved stakeholders by taking advantage of human creativity. The proposed approach has the capacity to instantiate cognitive cities by including cognitive computing. A use case in the context of decision-finding (concerning a transportation system) is presented to illustrate the approach.

Immune cell trafficking across the barriers of the central nervous system in multiple sclerosis and stroke

Each year about 650,000 Europeans die from stroke and a similar number lives with the sequelae of multiple sclerosis (MS). Stroke and MS differ in their etiology. Although cause and likewise clinical presentation set the two diseases apart, they share common downstream mechanisms that lead to damage and recovery. Demyelination and axonal injury are characteristics of MS but are also observed in stroke. Conversely, hallmarks of stroke, such as vascular impairment and neurodegeneration, are found in MS. However, the most conspicuous common feature is the marked neuroinflammatory response, marked by glia cell activation and immune cell influx. In MS and stroke the blood-brain barrier is disrupted allowing bone marrow-derived macrophages to invade the brain in support of the resident microglia. In addition, there is a massive invasion of auto-reactive T-cells into the brain of patients with MS. Though less pronounced a similar phenomenon is also found in ischemic lesions. Not surprisingly, the two diseases also resemble each other at the level of gene expression and the biosynthesis of other proinflammatory mediators. While MS has traditionally been considered to be an autoimmune neuroinflammatory disorder, the role of inflammation for cerebral ischemia has only been recognized later. In the case of MS the long track record as neuroinflammatory disease has paid off with respect to treatment options. There are now about a dozen of approved drugs for the treatment of MS that specifically target neuroinflammation by modulating the immune system. Interestingly, experimental work demonstrated that drugs that are in routine use to mitigate neuroinflammation in MS may also work in stroke models. Examples include Fingolimod, glatiramer acetate, and antibodies blocking the leukocyte integrin VLA-4. Moreover, therapeutic strategies that were discovered in experimental autoimmune encephalomyelitis (EAE), the animal model of MS, turned out to be also effective in experimental stroke models. This suggests that previous achievements in MS research may be relevant for stroke. Interestingly, the converse is equally true. Concepts on the neurovascular unit that were developed in a stroke context turned out to be applicable to neuroinflammatory research in MS. Examples include work on the important role of the vascular basement membrane and the BBB for the invasion of immune cells into the brain. Furthermore, tissue plasminogen activator (tPA), the only established drug treatment in acute stroke, modulates the pathogenesis of MS. Endogenous tPA is released from endothelium and astroglia and acts on the BBB, microglia and other neuroinflammatory cells. Thus, the vascular perspective of stroke research provides important input into the mechanisms on how endothelial cells and the BBB regulate inflammation in MS, particularly the invasion of immune cells into the CNS. In the current review we will first discuss pathogenesis of both diseases and current treatment regimens and will provide a detailed overview on pathways of immune cell migration across the barriers of the CNS and the role of activated astrocytes in this process. This article is part of a Special Issue entitled: Neuro inflammation: A common denominator for stroke, multiple sclerosis and Alzheimer's disease, guest edited by Helga de Vries and Markus Swaninger.

The role of plasmacytoid dendritic cells in the regulation of adaptive immunity

Plasmacytoid dendritic cells (pDCs) selectively express TLR7 which allows them to respond to RNA viruses and TLR9 which allows them to respond to DNA viruses and CpG oligonucleotides. Upon exposure to virus pDCs produce vast amounts of type I interferon (IFN) directly inhibiting viral replication and contributing to the activation of other immune cells. The ability of pDCs to promote B and T cell differentiation through type I IFN has been well documented although the role of additional factors including tumor necrosis factor (TNF) family members has not been thoroughly addressed. Here the expression of selected TNF family members in pDCs was examined and the role of TNF receptor-ligand interactions in the regulation of B and T lymphocyte growth and differentiation by pDCs was investigated. Upon stimulation with CpG-B, pDCs exhibit strong and stable expression of CD70, a TNF family ligand that binds to its receptor CD27 on memory B cells and promotes plasma cell differentiation and Ig secretion. Using an in vitro pDC/B cell co-culture system, it was determined that CpG-B-stimulated pDCs induce the proliferation of CD40L-activated human peripheral B cells and Ig secretion. This occurs independently of IFN and residual CpG, and requires physical contact between pDCs and B cells. CpG-stimulated pDCs induce the proliferation of both naive and memory B cells although Ig secretion is restricted to the memory subset. Blocking the interaction of CD70 with CD27 using an antagonist anti-CD70 antibody reduces the induction of B cell proliferation and IgG secretion by CpG-B-stimulated pDCs. Published studies have also indicated an important role for CD70 in promoting the expansion of CD4+ and CD8+ T cells and the development of effector function. CpG-B-stimulated pDCs induce naïve CD4+ T cell proliferation and production of multiple cytokines including IFN-γ, TNF-α, IL-10, IL-4, IL-5 and IL-13. Blocking the function of CD70 with an antagonist anti-CD70 antibody significantly reduced the induction of naïve CD4+ T cell proliferation by CpG-B-stimulated pDCs and the production of IL-4 and IL-13. Collectively these data indicate an important role for CD70 in the regulation of B and T lymphocyte growth and differentiation by pDCs. ^

Adaptive variability to low-pH river discharges in Acartia tonsa and stress responses to high PCO2 conditions

Environmental transitions leading to spatial physical-chemical gradients are of ecological and evolutionary interest because they are able to induce variations in phenotypic plasticity. Thus, the adaptive variability to low-pH river discharges may drive divergent stress responses [ingestion rates (IR) and expression of stress-related genes such as Heat shock protein 70 (Hsp70) and Ferritin] in the neritic copepod Acartia tonsa facing changes in the marine chemistry associated to ocean acidification (OA). These responses were tested in copepod populations inhabiting two environments with contrasting carbonate system parameters (an estuarine versus coastal area) in the Southern Pacific Ocean, and assessing an in situ and 96-h experimental incubation under conditions of high pressure of CO2 (PCO2 1200 ppm). Adaptive variability was a determining factor in driving variability of copepods' responses. Thus, the food-rich but colder and corrosive estuary induced a traits trade-off expressed as depressed IR under in situ conditions. However, this experience allowed these copepods to tolerate further exposure to high PCO2 levels better, as their IRs were on average 43% higher than those of the coastal individuals. Indeed, expression of both the Hsp70 and Ferritin genes in coastal copepods was significantly higher after acclimation to high PCO2 conditions. Along with other recent evidence, our findings confirm that adaptation to local fluctuations in seawater pH seems to play a significant role in the response of planktonic populations to OA-associated conditions. Facing the environmental threat represented by the inter-play between multiple drivers of climate change, this biological feature should be examined in detail as a potential tool for risk mitigation policies in coastal management arrangements.

Long-term dynamics of adaptive evolution in a globally important coccolithophore to ocean acidification

Recent evolution experiments have revealed that marine phytoplankton may adapt to global change, for example to ocean warming or acidification. Long-term adaptation to novel environments is a dynamic process and phenotypic change can take place thousands of generations after exposure to novel conditions. Using the longest evolution experiment performed in any marine species to date (4 yrs, = 2100 generations), we show that in the coccolithophore Emiliania huxleyi, long-term adaptation to ocean acidification is complex and initial phenotypic responses may revert for important traits. While fitness increased continuously, calcification was restored within the first 500 generations but later reduced in response to selection, enhancing physiological declines of calcification in response to ocean acidification. Interestingly, calcification was not constitutively reduced but revealed rates similar to control treatments when transferred back to present-day CO2 conditions. Growth rate increased with time in controls and adaptation treatments, although the effect size of adaptation assessed through reciprocal assay experiments varied. Several trait changes were associated with selection for higher cell division rates under laboratory conditions, such as reduced cell size and lower particulate organic carbon content per cell. Our results show that phytoplankton may evolve phenotypic plasticity that can affect biogeochemically important traits, such as calcification, in an unforeseen way under future ocean conditions.

Ecosystem regimes and responses in a coupled ancient lake system from MIS 5b to present: the diatom record of lakes Ohrid and Prespa

We reconstruct the aquatic ecosystem interactions since the last interglacial period in the oldest, most diverse, hydrologically connected European lake system, by using palaeolimnological diatom and selected geochemistry data from Lake Ohrid “DEEP site” core and equivalent data from Lake Prespa core, Co1215. Driven by climate forcing, the lakes experienced two adaptive cycles during the last 92 ka: "interglacial and interstadial" and "glacial" cycle. The short-term ecosystems reorganizations, e.g. regime shifts within these cycles substantially differ between the lakes, as evident from the inferred amplitudes of variation. The deeper Lake Ohrid shifted between ultra oligo- and oligotrophic regimes in contrast to the much shallower Lake Prespa, which shifted from a deeper, (oligo-) mesotrophic to a shallower, eutrophic lake and vice versa. Due to the high level of ecosystem stability (e.g. trophic state, lake level), Lake Ohrid appears relatively resistant to external forcing, such as climate and environmental change. Recovering in a relatively short time from major climate change, Lake Prespa is a resilient ecosystem. At the DEEP site, the decoupling between the lakes' response to climate change is marked in the prolonged and gradual changes during the MIS 5/4 and 2/1 transitions. These response differences and the lakes' different physical and chemical properties may limit the influence of Lake Prespa on Lake Ohrid. Regime shifts of Lake Ohrid due to potential hydrological change in Lake Prespa are not evident in the data presented here. Moreover, a complete collapse of the ecosystems functionality and loss of their diatom communities did not happen in either lake for the period presented in the study.

Some geometrical methods for constructing contradiction measures on Atanassov's intuitionistic fuzzy sets

Trillas et al. (1999, Soft computing, 3 (4), 197–199) and Trillas and Cubillo (1999, On non-contradictory input/output couples in Zadeh's CRI proceeding, 28–32) introduced the study of contradiction in the framework of fuzzy logic because of the significance of avoiding contradictory outputs in inference processes. Later, the study of contradiction in the framework of Atanassov's intuitionistic fuzzy sets (A-IFSs) was initiated by Cubillo and Castiñeira (2004, Contradiction in intuitionistic fuzzy sets proceeding, 2180–2186). The axiomatic definition of contradiction measure was stated in Castiñeira and Cubillo (2009, International journal of intelligent systems, 24, 863–888). Likewise, the concept of continuity of these measures was formalized through several axioms. To be precise, they defined continuity when the sets ‘are increasing’, denominated continuity from below, and continuity when the sets ‘are decreasing’, or continuity from above. The aim of this paper is to provide some geometrical construction methods for obtaining contradiction measures in the framework of A-IFSs and to study what continuity properties these measures satisfy. Furthermore, we show the geometrical interpretations motivating the measures.

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process

Pond head level control in a run-of-river hydro power plant using fuzzy controller

The run-of-river hydro power plant usually have low or nil water storage capacity, and therefore an adequate control strategy is required to keep the water level constant in pond. This paper presents a novel technique based on TSK fuzzy controller to maintain the pond head constant. The performance is investigated over a wide range of hill curve of hydro turbine. The results are compared with PI controller as discussed in [1].

Reduced order adaptive models for systems of PDEs using POD

In this work we propose a method to accelerate time dependent numerical solvers of systems of PDEs that require a high cost in computational time and memory. The method is based on the combined use of such numerical solver with a proper orthogonal decomposition, from which we identify modes, a Galerkin projection (that provides a reduced system of equations) and the integration of the reduced system, studying the evolution of the modal amplitudes. We integrate the reduced model until our a priori error estimator indicates that our approximation in not accurate. At this point we use again our original numerical code in a short time interval to adapt the POD manifold and continue then with the integration of the reduced model. Application will be made to two model problems: the Ginzburg-Landau equation in transient chaos conditions and the two-dimensional pulsating cavity problem, which describes the motion of liquid in a box whose upper wall is moving back and forth in a quasi-periodic fashion. Finally, we will discuss a way of improving the performance of the method using experimental data or information from numerical simulations

A Visual AGV-Urban Car using Fuzzy Control

The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Automatic Inference of Determinacy and Mutual Exclusion for Logic Programs Using Mode and Type Analyses

We propose an analysis for detecting procedures and goals that are deterministic (i.e., that produce at most one solution at most once),or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic. The analysis takes advantage of the pruning operator in order to improve the detection of mutual exclusion and determinacy. It also supports arithmetic equations and disequations, as well as equations and disequations on terms,for which we give a complete satisfiability testing algorithm, w.r.t. available type information. Information about determinacy can be used for program debugging and optimization, resource consumption and granularity control, abstraction carrying code, etc. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efficient.