A Structure of Problem Solving Methods for Real Time Decision Support in Traffic Control

This article describes a knowledge-based application in the domain of road traffic management that we have developed following a knowledge modeling approach and the notion of problem-solving method. The article presents first a domain-independent model for real-time decision support as a structured collection of problem solving methods. Then, it is described how this general model is used to develop an operational version for the domain of traffic management. For this purpose, a particular knowledge modeling tool, called KSM (Knowledge Structure Manager), was applied. Finally, the article shows an application developed for a traffic network of the city of Madrid and it is compared with a second application developed for a different traffic area of the city of Barcelona.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

The Plant Vesicle-associated SNARE AtVTI1a Likely Mediates Vesicle Transport from the Trans-Golgi Network to the Prevacuolar Compartment

Membrane traffic in eukaryotic cells relies on recognition between v-SNAREs on transport vesicles and t-SNAREs on target membranes. Here we report the identification of AtVTI1a and AtVTI1b, two Arabidopsis homologues of the yeast v-SNARE Vti1p, which is required for multiple transport steps in yeast. AtVTI1a and AtVTI1b share 60% amino acid identity with one another and are 32 and 30% identical to the yeast protein, respectively. By suppressing defects found in specific strains of yeast vti1 temperature-sensitive mutants, we show that AtVTI1a can substitute for Vti1p in Golgi-to-prevacuolar compartment (PVC) transport, whereas AtVTI1b substitutes in two alternative pathways: the vacuolar import of alkaline phosphatase and the so-called cytosol-to-vacuole pathway used by aminopeptidase I. Both AtVTI1a and AtVTI1b are expressed in all major organs of Arabidopsis. Using subcellular fractionation and immunoelectron microscopy, we show that AtVTI1a colocalizes with the putative vacuolar cargo receptor AtELP on the trans-Golgi network and the PVC. AtVTI1a also colocalizes with the t-SNARE AtPEP12p to the PVC. In addition, AtVTI1a and AtPEP12p can be coimmunoprecipitated from plant cell extracts. We propose that AtVTI1a functions as a v-SNARE responsible for targeting AtELP-containing vesicles from the trans-Golgi network to the PVC, and that AtVTI1b is involved in a different membrane transport process.

Clathrin Assembly Lymphoid Myeloid Leukemia (CALM) Protein: Localization in Endocytic-coated Pits, Interactions with Clathrin, and the Impact of Overexpression on Clathrin-mediated Traffic

The clathrin assembly lymphoid myeloid leukemia (CALM) gene encodes a putative homologue of the clathrin assembly synaptic protein AP180. Hence the biochemical properties, the subcellular localization, and the role in endocytosis of a CALM protein were studied. In vitro binding and coimmunoprecipitation demonstrated that the clathrin heavy chain is the major binding partner of CALM. The bulk of cellular CALM was associated with the membrane fractions of the cell and localized to clathrin-coated areas of the plasma membrane. In the membrane fraction, CALM was present at near stoichiometric amounts relative to clathrin. To perform structure–function analysis of CALM, we engineered chimeric fusion proteins of CALM and its fragments with the green fluorescent protein (GFP). GFP–CALM was targeted to the plasma membrane–coated pits and also found colocalized with clathrin in the Golgi area. High levels of expression of GFP–CALM or its fragments with clathrin-binding activity inhibited the endocytosis of transferrin and epidermal growth factor receptors and altered the steady-state distribution of the mannose-6-phosphate receptor in the cell. In addition, GFP–CALM overexpression caused the loss of clathrin accumulation in the trans-Golgi network area, whereas the localization of the clathrin adaptor protein complex 1 in the trans-Golgi network remained unaffected. The ability of the GFP-tagged fragments of CALM to affect clathrin-mediated processes correlated with the targeting of the fragments to clathrin-coated areas and their clathrin-binding capacities. Clathrin–CALM interaction seems to be regulated by multiple contact interfaces. The C-terminal part of CALM binds clathrin heavy chain, although the full-length protein exhibited maximal ability for interaction. Altogether, the data suggest that CALM is an important component of coated pit internalization machinery, possibly involved in the regulation of clathrin recruitment to the membrane and/or the formation of the coated pit.

Selective Perturbation of Apical Membrane Traffic by Expression of Influenza M2, an Acid-activated Ion Channel, in Polarized Madin–Darby Canine Kidney Cells

The function of acidification along the endocytic pathway is not well understood, in part because the perturbants used to modify compartmental pH have global effects and in some cases alter cytoplasmic pH. We have used a new approach to study the effect of pH perturbation on postendocytic traffic in polarized Madin–Darby canine kidney (MDCK) cells. Influenza M2 is a small membrane protein that functions as an acid-activated ion channel and can elevate the pH of the trans-Golgi network and endosomes. We used recombinant adenoviruses to express the M2 protein of influenza virus in polarized MDCK cells stably transfected with the polymeric immunoglobulin (Ig) receptor. Using indirect immunofluorescence and immunoelectron microscopy, M2 was found to be concentrated at the apical plasma membrane and in subapical vesicles; intracellular M2 colocalized partly with internalized IgA in apical recycling endosomes as well as with the trans-Golgi network marker TGN-38. Expression of M2 slowed the rate of IgA transcytosis across polarized MDCK monolayers. The delay in transport occurred after IgA reached the apical recycling endosome, consistent with the localization of intracellular M2. Apical recycling of IgA was also slowed in the presence of M2, whereas basolateral recycling of transferrin and degradation of IgA were unaffected. By contrast, ammonium chloride affected both apical IgA and basolateral transferrin release. Together, our data suggest that M2 expression selectively perturbs acidification in compartments involved in apical delivery without disrupting other postendocytic transport steps.

MAL, an Integral Element of the Apical Sorting Machinery, Is an Itinerant Protein That Cycles between the Trans-Golgi Network and the Plasma Membrane

The MAL proteolipid is a nonglycosylated integral membrane protein found in glycolipid-enriched membrane microdomains. In polarized epithelial Madin-Darby canine kidney cells, MAL is necessary for normal apical transport and accurate sorting of the influenza virus hemagglutinin. MAL is thus part of the integral machinery for glycolipid-enriched membrane–mediated apical transport. At steady state, MAL is predominantly located in perinuclear vesicles that probably arise from the trans-Golgi network (TGN). To act on membrane traffic and to prevent their accumulation in the target compartment, integral membrane elements of the protein-sorting machinery should be itinerant proteins that cycle between the donor and target compartments. To establish whether MAL is an itinerant protein, we engineered the last extracellular loop of MAL by insertion of sequences containing the FLAG epitope or with sequences containing residues that became O-glycosylated within the cells or that displayed biotinylatable groups. The ectopic expression of these modified MAL proteins allowed us to investigate the surface expression of MAL and its movement through different compartments after internalization with the use of a combination of assays, including surface biotinylation, surface binding of anti-FLAG antibodies, neuraminidase sensitivity, and drug treatments. Immunofluorescence and flow cytometric analyses indicated that, in addition to its Golgi localization, MAL was also expressed on the cell surface, from which it was rapidly internalized. This retrieval implies transport through the endosomal pathway and requires endosomal acidification, because it can be inhibited by drugs such as chloroquine, monensin, and NH4Cl. Resialylation experiments of surface MAL treated with neuraminidase indicated that ∼30% of the internalized MAL molecules were delivered to the TGN, probably to start a new cycle of cargo transport. Together, these observations suggest that, as predicted for integral membrane members of the late protein transport machinery, MAL is an itinerant protein cycling between the TGN and the plasma membrane.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Net Neutrality and Mandatory Network-Sharing: How to disconnect the continent. CEPS Policy Brief No. 309, 18 December 2013

A lively debate emerged on the proposed “Connected Continent” legislative package presented by the European Commission in September 2013. The package contains a proposed rule on the ‘open Internet’, which was heavily discussed in European Parliament hearings in early December. This commentary argues that while the proposed rule is in principle balanced and appealing, it is utterly impractical due to the enormous uncertainty that its application would entail. At the same time, the rule is very far from what neutrality proponents have argued for almost a decade: rather than the place for internet freedom, it would transform the Web into a place requiring constant micro-management and tutoring of user behaviour. Both arguments lead to the conclusion that the current proposal should be at once reformed and analysed under a more holistic lens. On the one hand, Europe should launch an ambitious project for the future, converged infrastructure by mobilising resources and reforming rules to encourage investment into ubiquitous, converged, ‘always on’ connectivity. On the other hand, enhanced legal certainty for broadband investment could justify a more neutrality-oriented approach to traffic management practices on the Internet. The author proposes a new approach to Internet regulation which, altogether, will lead to a more balanced and sustainable model for the future, without jeopardising user freedom.

Construction of a comprehensive causal network. Volume 2: literature review and bibliographies. Phase II report.

National Highway Traffic Safety Administration, Washington, D.C.

A methodology for the assessment of truck lane needs in the Texas highway network. Final report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Network assignment methods for the analysis of truck-related highway improvements. Interim report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Truck lane needs methodology: a heuristic approach to solve a five option discrete network design problem. Interim report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Construction of a comprehensive causal network, phase III: final report (volume I).

National Highway Traffic Safety Administration, Washington, D.C.