Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures.

Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness (SW) metrics, respectively. Yet the number of PNES attacks per month correlated with a weakness of local connectedness and a skewed balance between local and global connectedness quantified with SW, all in EEG alpha band. In beta band, patients demonstrated above-normal resiliency, measured with assortativity coefficient, which also correlated with the frequency of PNES attacks. This interictal EEG phenotype may help improve differentiation between PNES and epilepsy. The results also suggest that local connectivity could be a target for therapeutic interventions in PNES. Selective modulation (strengthening) of local connectivity might improve the skewed balance between local and global connectivity and so prevent PNES events.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

The policy-making structure of European regulatory networks and the domestic adoption of standards

European regulatory networks (ERNs) constitute the main governance instrument for the informal co-ordination of public regulation at the European Union (EU) level. They are in charge of co-ordinating national regulators and ensuring the implementation of harmonized regulatory policies across the EU, while also offering sector-specific expertise to the Commission. To this aim, ERNs develop 'best practices' and benchmarking procedures in the form of standards, norms and guidelines to be adopted in member states. In this paper, we focus on the Committee of European Securities Regulators and examine the consequences of the policy-making structure of ERNs on the domestic adoption of standards. We find that the regulators of countries with larger financial industries tend to occupy more central positions in the network, especially among newer member states. In turn, network centrality is associated with a more prompt domestic adoption of standards.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Comparison of early cortical networks in efficient and inefficient visual search: an event-related potential study.

Functional magnetic resonance imaging studies have indicated that efficient feature search (FS) and inefficient conjunction search (CS) activate partially distinct frontoparietal cortical networks. However, it remains a matter of debate whether the differences in these networks reflect differences in the early processing during FS and CS. In addition, the relationship between the differences in the networks and spatial shifts of attention also remains unknown. We examined these issues by applying a spatio-temporal analysis method to high-resolution visual event-related potentials (ERPs) and investigated how spatio-temporal activation patterns differ for FS and CS tasks. Within the first 450 msec after stimulus onset, scalp potential distributions (ERP maps) revealed 7 different electric field configurations for each search task. Configuration changes occurred simultaneously in the two tasks, suggesting that contributing processes were not significantly delayed in one task compared to the other. Despite this high spatial and temporal correlation, two ERP maps (120-190 and 250-300 msec) differed between the FS and CS. Lateralized distributions were observed only in the ERP map at 250-300 msec for the FS. This distribution corresponds to that previously described as the N2pc component (a negativity in the time range of the N2 complex over posterior electrodes of the hemisphere contralateral to the target hemifield), which has been associated with the focusing of attention onto potential target items in the search display. Thus, our results indicate that the cortical networks involved in feature and conjunction searching partially differ as early as 120 msec after stimulus onset and that the differences between the networks employed during the early stages of FS and CS are not necessarily caused by spatial attention shifts.

Error and attack tolerance of layered complex networks.

Many complex systems may be described by not one but a number of complex networks mapped on each other in a multi-layer structure. Because of the interactions and dependencies between these layers, the state of a single layer does not necessarily reflect well the state of the entire system. In this paper we study the robustness of five examples of two-layer complex systems: three real-life data sets in the fields of communication (the Internet), transportation (the European railway system), and biology (the human brain), and two models based on random graphs. In order to cover the whole range of features specific to these systems, we focus on two extreme policies of system's response to failures, no rerouting and full rerouting. Our main finding is that multi-layer systems are much more vulnerable to errors and intentional attacks than they appear from a single layer perspective.

The adequacy of artificial oviposition substrates for laboratory rearing of Piezodorus guildinii (Westwood) (Heteroptera, Pentatomidae)

Studies in the laboratory tested the suitability of synthetic wool string, cotton string, cheesecloth, and commercial cotton ball as artificial oviposition substrates for the small green stink bug, Piezodorus guildinii (Westwood) (Heteroptera, Pentatomidae). In confined cages, 54% of total egg masses was laid on synthetic wool string, 31% on cotton string, and only 15% on cheesecloth. In an additional test, the best substrate selected, synthetic wool string, received 92% of egg masses compared to 8% on the commonly used substrate, cotton ball. Synthetic wool string received the most egg masses of any size, in particular those in the range 11-20 eggs/mass. Because the eggs of P. guildinii are laid in two parallel double rows, the egg masses fit the wool string perfectly.

Statistics in archaeology: New directions

Connections between Statistics and Archaeology have always appeared veryfruitful. The objective of this paper is to offer an outlook of somestatistical techniques that are being developed in the most recentyears and that can be of interest for archaeologists in the short run.

Selection of artificial hosts for oviposition by wild Anastrepha obliqua (Macquart) (Diptera, Tephritidae): influence of adult food and effect of experience

Several factors influence the selection of oviposition substrates by insects. The aim of the present work was to find answers to the following questions related to the oviposition behavior of Anastrepha obliqua. Can carbohydrates (glucose or sucrose) present in the adult diet have influence on the female preference for an oviposition substrate with similar composition? Can the previous experience with a host containing one of mentioned carbohydrates interfere in further selection of oviposition substrates? The results showed that the kind of carbohydrate present in the adult diet did not affect the female preference for an artificial oviposition substrate, neither when it was presented by itself nor in combination with brewer's yeast. The effect of experience in the oviposition behavior was observed when there was a previous contact with artificial oviposition substrates containing yeast and sucrose. The data are discussed in terms of the behavioral plasticity presented by this species in relation to feeding and oviposition behaviors.

Optimization of multiclass queueing networks with changeover times via the achievable region approach: Part I, the single-station case

We address the performance optimization problem in a single-stationmulticlass queueing network with changeover times by means of theachievable region approach. This approach seeks to obtainperformance bounds and scheduling policies from the solution of amathematical program over a relaxation of the system's performanceregion. Relaxed formulations (including linear, convex, nonconvexand positive semidefinite constraints) of this region are developedby formulating equilibrium relations satisfied by the system, withthe help of Palm calculus. Our contributions include: (1) newconstraints formulating equilibrium relations on server dynamics;(2) a flow conservation interpretation of the constraintspreviously derived by the potential function method; (3) newpositive semidefinite constraints; (4) new work decomposition lawsfor single-station multiclass queueing networks, which yield newconvex constraints; (5) a unified buffer occupancy method ofperformance analysis obtained from the constraints; (6) heuristicscheduling policies from the solution of the relaxations.

Optimization of multiclass queueing networks with changeover times via the achievable region method: Part II, the multi-station case

We address the problem of scheduling a multi-station multiclassqueueing network (MQNET) with server changeover times to minimizesteady-state mean job holding costs. We present new lower boundson the best achievable cost that emerge as the values ofmathematical programming problems (linear, semidefinite, andconvex) over relaxed formulations of the system's achievableperformance region. The constraints on achievable performancedefining these formulations are obtained by formulatingsystem's equilibrium relations. Our contributions include: (1) aflow conservation interpretation and closed formulae for theconstraints previously derived by the potential function method;(2) new work decomposition laws for MQNETs; (3) new constraints(linear, convex, and semidefinite) on the performance region offirst and second moments of queue lengths for MQNETs; (4) a fastbound for a MQNET with N customer classes computed in N steps; (5)two heuristic scheduling policies: a priority-index policy, anda policy extracted from the solution of a linear programmingrelaxation.