Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Digital business reporting standards:mapping the battle in France

Government agencies use information technology extensively to collect business data for regulatory purposes. Data communication standards form part of the infrastructure with which businesses must conform to survive. We examine the development of, and emerging competition between, two open business reporting data standards adopted by government bodies in France; EDIFACT (incumbent) and XBRL (challenger). The research explores whether an incumbent may be displaced in a setting in which the contention is unresolved. We apply Latour’s (1992) translation map to trace the enrolments and detours in the battle. We find that regulators play an important role as allies in the development of the standards. The antecedent networks in which the standards are located embed strong beliefs that become barriers to collaboration and fuel the battle. One of the key differentiating attitudes is whether speed is more important than legitimacy. The failure of collaboration encourages competition. The newness of XBRL’s technology just as regulators need to respond to an economic crisis and its adoption by French regulators not using EDIFACT create an opportunity for the challenger to make significant network gains over the longer term. ANT also highlights the importance of the preservation of key components of EDIFACT in ebXML.

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

Flexible pavement layer moduli determination : an adaptive artificial neural network approach

The estimation of pavement layer moduli through the use of an artificial neural network is a new concept which provides a less strenuous strategy for backcalculation procedures. Artificial Neural Networks are biologically inspired models of the human nervous system. They are specifically designed to carry out a mapping characteristic. This study demonstrates how an artificial neural network uses non-destructive pavement test data in determining flexible pavement layer moduli. The input parameters include plate loadings, corresponding sensor deflections, temperature of pavement surface, pavement layer thicknesses and independently deduced pavement layer moduli.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Measuring the Diversity and the Integration of Film Production in Mercosur: A Social Network Analysis

Based on an original and comprehensive database of all feature fiction films produced in Mercosur between 2004 and 2012, the paper analyses whether the Mercosur film industry has evolved towards an integrated and culturally more diverse market. It provides a summary of policy opportunities in terms of integration and diversity, emphasizing the limiter role played by regional policies. It then shows that although the Mercosur film industry remains rather disintegrated, it tends to become more integrated and culturally more diverse. From a methodological point of view, the combination of Social Network Analysis and the Stirling Model opens up interesting research tracks to analyse creative industries in terms of their market integration and their cultural diversity.

D6.6 – Summary, Analysis, Road-mapping and Production of Training materials

This deliverable summarizes, validates and explains the purpose and concept behind the RAGE knowledge and innovation management platform as a self-sustainable Ecosystem, supporting innovation processes in the Applied Gaming (AG) industry. The Ecosystem portal will be developed with particular consideration of the demand and requirements of small and medium sized game developing companies, education providers and related stakeholders like AG researchers and AG end-users. The innovation potential of the new platform underlies the following factors: a huge, mostly entire collection of community specific knowledge (e.g., content like media objects, software components and best practices), a structured approach of knowledge access, search and browse, collaboration tools as well as social network analysis tools to foster efficient knowledge creation and transformation processes into marketable technology assets. The deliverable provides an overview of the current status and the remaining work to come, preceding the final version in month 48 of the RAGE project.

Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome.

PURPOSE: Myeloma is a clonal malignancy of plasma cells. Poor-prognosis risk is currently identified by clinical and cytogenetic features. However, these indicators do not capture all prognostic information. Gene expression analysis can be used to identify poor-prognosis patients and this can be improved by combination with information about DNA-level changes. EXPERIMENTAL DESIGN: Using single nucleotide polymorphism-based gene mapping in combination with global gene expression analysis, we have identified homozygous deletions in genes and networks that are relevant to myeloma pathogenesis and outcome. RESULTS: We identified 170 genes with homozygous deletions and corresponding loss of expression. Deletion within the "cell death" network was overrepresented and cases with these deletions had impaired overall survival. From further analysis of these events, we have generated an expression-based signature associated with shorter survival in 258 patients and confirmed this signature in data from two independent groups totaling 800 patients. We defined a gene expression signature of 97 cell death genes that reflects prognosis and confirmed this in two independent data sets. CONCLUSIONS: We developed a simple 6-gene expression signature from the 97-gene signature that can be used to identify poor-prognosis myeloma in the clinical environment. This signature could form the basis of future trials aimed at improving the outcome of poor-prognosis myeloma.

Future(s) Perfect: uchronian mapping as a research and visualisation tool in the fringes of the Olympic Park

This article explores and discusses the development of a mapping tool inspired by Charles Renouvier’s philosophical novel Uchronie (l’utopie dans l’histoire) (1876). The article explains the research and design process of creating a uchronian map of a formerly empty site in Fish Island in East London and describes a participatory workshop titled ‘Hackney Wick and Fish Island: Future Perfect(s)’ (25 April 2015) that used uchronian mapping to explore past and future development imaginaries of two sites in the neighbourhood. Given a uchronian mapping template, a protocol and a dossier of planning and other documents, participants were encouraged to develop their own uchronian map of each site, and in doing so test and question the process of visualizing ‘what was supposed to happen’, ‘what actually happened’ and ‘what could have happened’. The article concludes with a reflection on uchronian mapping as a tool for researching, analysing and making visible urban alternatives.

Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001–2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products

An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to 2011. The modified algorithm used the Brightness Temperature channel from the Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 T31 (11.03 μm) instead of the Advanced Very High Resolution Radiometer (AVHRR) band T3 (3.75 μm). The accuracy of the BA-LTDR, the Collection 5.1 MODIS Burned Area (MCD45A1), the MODIS Collection 5.1 Direct Broadcast Monthly Burned Area (MCD64A1) and the Burned Area GEOLAND-2 (BA GEOLAND-2) products was assessed using reference data from the Alaska Fire Service (AFS) and the Canadian Forest Service National Fire Database (CFSNFD). The linear regression analysis of the burned area percentages of the MCD64A1 product using 40 km × 40 km grids versus the reference data for the years from 2001 to 2011 showed an agreement of R2 = 0.84 and a slope = 0.76, while the BA-LTDR showed an agreement of R2 = 0.75 and a slope = 0.69. These results represent an improvement over the MCD45A1 product, which showed an agreement of R2 = 0.67 and a slope = 0.42. The MCD64A1, BA-LTDR and MCD45A1 products underestimated the total burned area in the study region, whereas the BA GEOLAND-2 product overestimated it by approximately five-fold, with an agreement of R2 = 0.05. Despite MCD64A1 showing the best overall results, the BA-LTDR product proved to be an alternative for mapping burned areas in the North American boreal forest region compared with the other global BA products, even those with higher spatial/spectral resolution

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area