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This article is from Skeletal Muscle , volume 3 . Abstract Background: Duchenne muscular dystrophy (DMD) is one of the most frequent forms of muscular disorders. It is caused by the absence of dystrophin, a core component of the sarcolemma-associated junctional complex that links the cytoskeleton to the extracellular matrix. We showed previously that plectin 1f (P1f), one of the major muscle-expressed isoforms of the cytoskeletal linker protein plectin, accumulates at the sarcolemma of DMD patients as well as of mdx mice, a widely studied animal model for DMD.Based on plectin’s dual role as structural protein and scaffolding platform for signaling molecules, we speculated that the dystrophic phenotype observed after loss of dystrophin was caused, at least to some extent, by excess plectin. Thus, we hypothesized that elimination of plectin expression in mdx skeletal muscle, while probably resulting in an overall more severe phenotype, may lead to a partial phenotype rescue. In particular, we wanted to assess whether excess sarcolemmal plectin contributes to the dysregulation of sugar metabolism in mdx myofibers. Methods: We generated plectin/dystrophin double deficient (dKO) mice by breeding mdx with conditional striated muscle-restricted plectin knockout (cKO) mice. The phenotype of these mice was comparatively analyzed with that of mdx, cKO, and wild-type mice, focusing on structural integrity and dysregulation of glucose metabolism. Results: We show that the accumulation of plectin at the sarcolemma of mdx muscle fibers hardly compensated for their loss of structural integrity. Instead, it led to an additional metabolic deficit by impairing glucose uptake. While dKO mice suffered from an overall more severe form of muscular dystrophy compared to mdx or plectin-deficient mice, sarcolemmal integrity as well as glucose uptake of their myofibers were restored to normal levels upon ablation of plectin. Furthermore, microtubule (MT) networks in intact dKO myofibers, including subsarcolemmal areas, were found to be more robust than those in mdx mice. Finally, myotubes differentiated from P1f-overexpressing myoblasts showed an impairment of glucose transporter 4 translocation and a destabilization of MT networks. Conclusions: Based on these results we propose that sarcolemma-associated plectin acts as an antagonist of MT network formation in myofibers, thereby hindering vesicle-mediated (MT-dependent) transport of glucose transporter 4. This novel role of plectin throws a bridge between extra-sarcomeric cytoarchitecture and metabolism of muscle fibers. Our study thus provides new insights into pathomechanisms of plectinopathies and muscular dystrophies in general.

A major challenge in neuroimaging is understanding the mapping of neurophysiological dynamics onto cognitive functions. Traditionally, these maps have been constructed by examining changes in the activity magnitude of regions related to task performance. Recently, network neuroscience has produced methods to map connectivity patterns among many regions to certain cognitive functions by drawing on tools from network science and graph theory. However, these two different views are rarely addressed simultaneously, largely because few tools exist that account for patterns between nodes while simultaneously considering activation of nodes. We address this gap by solving the problem of community detection on weighted networks with continuous (non-integer) annotations by deriving a generative probabilistic model. This model generates communities whose members connect densely to nodes within their own community, and whose members share similar annotation values. We demonstrate the utility of the model in the context of neuroimaging data gathered during a motor learning paradigm, where edges are task-based functional connectivity and annotations to each node are beta weights from a general linear model that encoded a linear decrease in blood-oxygen-level-dependent signal with practice. Interestingly, we observe that individuals who learn at a faster rate exhibit the greatest dissimilarity between functional connectivity and activation magnitudes, suggesting that activation and functional connectivity are distinct dimensions of neurophysiology that track behavioral change. More generally, the tool that we develop offers an explicit, mathematically principled link between functional activation and functional connectivity, and can readily be applied to a other similar problems in which one set of imaging data offers network data, and a second offers a regional attribute.

A major challenge in neuroimaging is understanding the mapping of neurophysiological dynamics onto cognitive functions. Traditionally, these maps have been constructed by examining changes in the activity magnitude of regions related to task performance. Recently, network neuroscience has produced methods to map connectivity patterns among many regions to certain cognitive functions by drawing on tools from network science and graph theory. However, these two different views are rarely addressed simultaneously, largely because few tools exist that account for patterns between nodes while simultaneously considering activation of nodes. We address this gap by solving the problem of community detection on weighted networks with continuous (non-integer) annotations by deriving a generative probabilistic model. This model generates communities whose members connect densely to nodes within their own community, and whose members share similar annotation values. We demonstrate the utility of the model in the context of neuroimaging data gathered during a motor learning paradigm, where edges are task-based functional connectivity and annotations to each node are beta weights from a general linear model that encoded a linear decrease in blood-oxygen-level-dependent signal with practice. Interestingly, we observe that individuals who learn at a faster rate exhibit the greatest dissimilarity between functional connectivity and activation magnitudes, suggesting that activation and functional connectivity are distinct dimensions of neurophysiology that track behavioral change. More generally, the tool that we develop offers an explicit, mathematically principled link between functional activation and functional connectivity, and can readily be applied to a other similar problems in which one set of imaging data offers network data, and a second offers a regional attribute.

Here we introduce a model in which individuals differ in the rate at which they seek new interactions with others, making rational decisions modeled as general symmetric two-player games. Once a link between two individuals has formed, the productivity of this link is evaluated. Links can be broken off at different rates. We provide analytic results for the limiting cases where linking dynamics is much faster than evolutionary dynamics and vice-versa, and show how the individual capacity of forming new links or severing inconvenient ones maps into the problem of strategy evolution in a well-mixed population under a different game. For intermediate ranges, we investigate numerically the detailed interplay determined by these two time-scales and show that the scope of validity of the analytical results extends to a much wider ratio of time scales than expected.

A novel concept using neural network technology was employed to find the correct linkage of ionospheic modes in a multi-mode multi-target environment given a model of the ionosphere. The technology employed consists of a self-organizing feature map (SOFM) to provide separation of multiple targets containing multiple modes followed by an Adaptive Resonance Theory Map (ARTMAP) to perform spatial pattern recognition of ionospheric mode patterns and to link them to a single ground range. The ARTMAP recognition system is trained on the Coordinate Registration (CR) tables produced by ROTHR. The current CR tables are updated every 12 minutes, thus providing a real-time assessment of the ionosphere. The exactness of the mode-linking procedure consequently depends on the accuracy of the Coordinate Registration tables.

We study network growth from a fixed set of initially isolated nodes placed at random on the surface of a sphere. The growth mechanism we use adds edges to the network depending on strictly local gain and cost criteria. Only nodes that are not too far apart on the sphere may be considered for being joined by an edge. Given two such nodes, the joining occurs only if the gain of doing it surpasses the cost. Our model is based on a multiplicative parameter lambda that regulates, in a function of node degrees, the maximum geodesic distance that is allowed between nodes for them to be considered for joining. For n nodes distributed uniformly on the sphere, and for lambda*sqrt(n) within limits that depend on cost-related parameters, we have found that our growth mechanism gives rise to power-law distributions of node degree that are invariant for constant lambda*sqrt(n). We also study connectivity- and distance-related properties of the networks.

There is an ever growing number of users with accounts on multiple social media and networking sites. Consequently, there is increasing interest in matching user accounts and profiles across different social networks in order to create aggregate profiles of users. In this paper, we present models for Digital Stylometry, which is a method for matching users through stylometry inspired techniques. We experimented with linguistic, temporal, and combined temporal-linguistic models for matching user accounts, using standard and novel techniques. Using publicly available data, our best model, a combined temporal-linguistic one, was able to correctly match the accounts of 31% of 5,612 distinct users across Twitter and Facebook.

This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.

The Research and Development Utilization (RDU) program research component examines the successes and administrative dilemmas faced by demonstration projects that attempted to develop interorganizational networks in a knowledge utilization and school improvement process. A framework is presented for looking at RDU projects as a group of interorganizational networks. Issues relevant to the design, management, effectiveness, and institutionalization of such networks are discussed, focusing on networking as a strategy for knowledge utilization in general, and for RDU in particular. Chapter-length case studies of four RDU projects examine the networking strategy in greater detail. These cases are then synthesized in the final chapter, and the lessons learned about networking are summarized for the future design and management of educational linkage systems. (Author/MLF)

The Research and Development Utilization (RDU) program research component examines the successes and administrative dilemmas faced by demonstration projects that attempted to develop interorganizational networks in a knowledge utilization and school improvement process. A framework is presented for looking at RDU projects as a group of interorganizational networks. Issues relevant to the design, management, effectiveness, and institutionalization of such networks are discussed, focusing on networking as a strategy for knowledge utilization in general, and for RDU in particular. Chapter-length case studies of four RDU projects examine the networking strategy in greater detail. These cases are then synthesized in the final chapter, and the lessons learned about networking are summarized for the future design and management of educational linkage systems. (Author/MLF)

We consider crossbar switching networks with base $b$ (that is, constructed from $b\times b$ crossbar switches), scale $k$ (that is, with $b^k$ inputs, $b^k$ outputs and $b^k$ links between each consecutive pair of stages) and depth $l$ (that is, with $l$ stages). We assume that the crossbars are interconnected according to the spider-web pattern, whereby two diverging paths reconverge only after at least $k$ stages. We assume that each vertex is independently idle with probability $q$, the vacancy probability. We assume that $b\ge 2$ and the vacancy probability $q$ are fixed, and that $k$ and $l = ck$ tend to infinity with ratio a fixed constant $c>1$. We consider the linking probability $Q$ (the probability that there exists at least one idle path between a given idle input and a given idle output). In a previous paper it was shown that if $c\le 2$, then the linking probability $Q$ tends to 0 if $0 1$. This is done by using generating functions and complex-variable techniques to estimate the second moments of various random variables involved in the analysis of the networks.

In the search for a covariant formulation for Loop Quantum Gravity, spin foams have arised as the corresponding discrete space-time structure and, among the different models, the Barrett-Crane model seems the most promising. Here, we study its boundary states and introduce cylindrical functions on both the Lorentz connection and the time normal to the studied hypersurface. We call them projected cylindrical functions and we explain how they would naturally arise in a covariant formulation of Loop Quantum Gravity.

In the search for a covariant formulation for Loop Quantum Gravity, spin foams have arised as the corresponding discrete space-time structure and, among the different models, the Barrett-Crane model seems the most promising. Here, we study its boundary states and introduce cylindrical functions on both the Lorentz connection and the time normal to the studied hypersurface. We call them projected cylindrical functions and we explain how they would naturally arise in a covariant formulation of Loop Quantum Gravity.

In the search for a covariant formulation for Loop Quantum Gravity, spin foams have arised as the corresponding discrete space-time structure and, among the different models, the Barrett-Crane model seems the most promising. Here, we study its boundary states and introduce cylindrical functions on both the Lorentz connection and the time normal to the studied hypersurface. We call them projected cylindrical functions and we explain how they would naturally arise in a covariant formulation of Loop Quantum Gravity.

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of {\it structure-driven activity} has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities---including those of different cell types---combine with connectivity to shape population activity and function.

A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).

The reliance upon interorganizational relationships in health and public health systems is extensive; however, there is a shortage of evidence that focuses on stakeholder network performance. Responding to this gap and the call for a systems approach, the study's first research question asks whether community based stakeholder networks operating within the same context exhibit commonalities in network-level attributes and characteristics relative to their performance. The second question focuses on organizational level attributes and characteristics, asking: do the relative connectivity and structural positions of member organizations affect stakeholder network performance?In a mixed method, cross case research design, the characteristics and attributes of five stakeholder networks were analyzed and compared. Whole-network, dyad and organizational measures were examined through network analysis, and analyzed with performance measures based on external stakeholder assessments, network self assessments and local early intervention assessments. Emergent trends were then analyzed through content analysis.Focusing on the dynamic interactions of public health agencies with public and private organizations that affect health, the five stakeholder groups are goal-directed networks working to ensure standardized developmental screening, referral and follow through for children in their Colorado communities. The stakeholder networks were mobilized to overcome the segmented system of early intervention services.The study found that there were no relationships in whole-network level attributes and characteristics relative to performance. Investigation into the second research question revealed a positive relationship between network structures with a system-building organization in a key position relative to network performance; and a negative relationship between network structures with a service provider organization in a key position relative to network performance. There was also a positive relationship between the levels of integration of primary care providers relative to network performance. Results also showed that trust among organizations within a stakeholder network is not a sufficient condition for network performance.The study findings provide new evidence about how different characteristics and attributes of stakeholder networks affect performance, adding to a much needed knowledge base to guide the use, formation and management of organizational networks for strong health and public health systems.