Test-retest reliability of graph theory measures of structural brain connectivity

The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and 'small-world' properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

Becoming-topologies of education: Deformations, networks and the database effect

This article uses topological approaches to suggest that education is becoming-topological. Analyses presented in a recent double-issue of Theory, Culture & Society are used to demonstrate the utility of topology for education. In particular, the article explains education's topological character through examining the global convergence of education policy, testing and the discursive ranking of systems, schools and individuals in the promise of reforming education through the proliferation of regimes of testing at local and global levels that constitute a new form of governance through data. In this conceptualisation of global education policy changes in the form and nature of testing combine with it the emergence of global policy network to change the nature of the local (national, regional, school and classroom) forces that operate through the ‘system’. While these forces change, they work through a discursivity that produces disciplinary effects, but in a different way. This new–old disciplinarity, or ‘database effect’, is here represented through a topological approach because of its utility for conceiving education in an increasingly networked world.

Isostructural co-crystals derived from molecules with different supramolecular topologies

In this article, we report the crystal structures of five halogen bonded co-crystals comprising quaternary ammonium cations, halide anions (Cl– and Br–), and one of either 1,2-, 1,3-, or 1,4-diiodotetrafluorobenzene (DITFB). Three of the co-crystals are chemical isomers: 1,4-DITFB[TEA-CH2Cl]Cl, 1,2-DITFB[TEA-CH2Cl]Cl, and 1,3-DITFB[TEA-CH2Cl]Cl (where TEA-CH2Cl is chloromethyltriethylammonium ion). In each structure, the chloride anions link DITFB molecules through halogen bonds to produce 1D chains propagating with (a) linear topology in the structure containing 1,4-DITFB, (b) zigzag topology with 60° angle of propagation in that containing 1,2-DITFB, and (c) 120° angle of propagation with 1,3-DITFB. While the individual chains have highly distinctive and different topologies, they combine through π-stacking of the DITFB molecules to produce remarkably similar overall arrangements of molecules. Structures of 1,4-DITFB[TEA-CH2Br]Br and 1,3-DITFB[TEA-CH2Br]Br are also reported and are isomorphous with their chloro/chloride analogues, further illustrating the robustness of the overall supramolecular architecture. The usual approach to crystal engineering is to make structural changes to molecular components to effect specific changes to the resulting crystal structure. The results reported herein encourage pursuit of a somewhat different approach to crystal engineering. That is, to investigate the possibilities for engineering the same overall arrangement of molecules in crystals while employing molecular components that aggregate with entirely different supramolecular connectivity.

A CCG virtual system for big data application communication costs analysis

Network topology and routing are two important factors in determining the communication costs of big data applications at large scale. As for a given Cluster, Cloud, or Grid system, the network topology is fixed and static or dynamic routing protocols are preinstalled to direct the network traffic. Users cannot change them once the system is deployed. Hence, it is hard for application developers to identify the optimal network topology and routing algorithm for their applications with distinct communication patterns. In this study, we design a CCG virtual system (CCGVS), which first uses container-based virtualization to allow users to create a farm of lightweight virtual machines on a single host. Then, it uses software-defined networking (SDN) technique to control the network traffic among these virtual machines. Users can change the network topology and control the network traffic programmingly, thereby enabling application developers to evaluate their applications on the same system with different network topologies and routing algorithms. The preliminary experimental results through both synthetic big data programs and NPB benchmarks have shown that CCGVS can represent application performance variations caused by network topology and routing algorithm.

A new load distribution strategy for linear network with communication delays

In this paper, we propose a new load distribution strategy called `send-and-receive' for scheduling divisible loads, in a linear network of processors with communication delay. This strategy is designed to optimally utilize the network resources and thereby minimizes the processing time of entire processing load. A closed-form expression for optimal size of load fractions and processing time are derived when the processing load originates at processor located in boundary and interior of the network. A condition on processor and link speed is also derived to ensure that the processors are continuously engaged in load distributions. This paper also presents a parallel implementation of `digital watermarking problem' on a personal computer-based Pentium Linear Network (PLN) topology. Experiments are carried out to study the performance of the proposed strategy and results are compared with other strategies found in literature.

A New Mechanism For Ferromagnetism In C-60-TDAE

Based on the topology of C-60 and the resulting non-disjoint nature of the lowest unoccupied molecular orbitals, Ne propose a new model for ferromagnetic exchange in C-60-TDAE. Within the Hubbard model, we find that the ferromagnetic exchange integral is stabilized to first order in the inter-ball transfer integral, while the antiferromagnetic coupling is stabilized only to second order. This difference is adequate to counter the larger phase space available for stabilizing the antiferromagnetic state. Thus, the ground state is found to be ferromagnetic for reasonable inter-ball transfer integrals.

Void Geometry Driven Spin Crossover in Zeolite-Encapsulated Cobalt Tris(bipyridyl) Complex Ion

The cobalt(II) tris(bipyridyl) complex ion encapsulated in zeolite-Y supercages exhibits a thermally driven interconversion between a low-spin and a high-spin state-a phenomenon not observed for this ion either in solid state or in solution. From a comparative study of the magnetism and optical spectroscopy of the encapsulated and unencapsulated complex ion, supported by molecular modeling, such spin behavior is shown to be intramolecular in origin. In the unencapsulated or free state, the [Co(bipy)(3)](2+) ion exhibits a marked trigonal prismatic distortion, but on encapsulation, the topology of the supercage forces it to adopt a near-octahedral geometry. An analysis using the angular overlap ligand field model with spectroscopically derived parameters shows that the geometry does indeed give rise to a low-spin ground state, and suggests a possible scenario for the spin state interconversion.

Solar powered high voltage energization for vehicular exhaust cleaning: A step towards possible retrofitting in vehicles

This paper proposes a novel way of generating high voltage for electric discharge plasma in controlling NOx emission in diesel engine exhaust. A solar powered high frequency electric discharge topology has been suggested that will improve the size and specific energy density required when compared to the traditional repetitive pulse or 50 Hz AC energization. This methodology has been designed, fabricated and experimentally verified by conducting studies on real diesel engine exhaust.

Efficient Output-Sensitive Construction of Reeb Graphs

The Reeb graph tracks topology changes in level sets of a scalar function and finds applications in scientific visualization and geometric modeling. This paper describes a near-optimal two-step algorithm that constructs the Reeb graph of a Morse function defined over manifolds in any dimension. The algorithm first identifies the critical points of the input manifold, and then connects these critical points in the second step to obtain the Reeb graph. A simplification mechanism based on topological persistence aids in the removal of noise and unimportant features. A radial layout scheme results in a feature-directed drawing of the Reeb graph. Experimental results demonstrate the efficiency of the Reeb graph construction in practice and its applications.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Latency, Power and Performance Trade-offs in Network-on-Chips by Link Microarchitecture Exploration

This paper presents a power, latency and throughput trade-off study on NoCs by varying microarchitectural (e.g. pipelining) and circuit level (e.g. frequency and voltage) parameters. We change pipelining depth, operating frequency and supply voltage for 3 example NoCs - 16 node 2D Torus, Tree network and Reduced 2D Torus. We use an in-house NoC exploration framework capable of topology generation and comparison using parameterized models of Routers and links developed in SystemC. The framework utilizes interconnect power and delay models from a low-level modelling tool called Intacte[1]1. We find that increased pipelining can actually reduce latency. We also find that there exists an optimal degree of pipelining which is the most energy efficient in terms of minimizing energy-delay product.

Algebraic and geometric intersection numbers for free groups

We show that the algebraic intersection number of Scott and Swarup for splittings of free groups Coincides With the geometric intersection number for the sphere complex of the connected sum of copies of S-2 x S-1. (C) 2009 Elsevier B.V. All rights reserved.

A New Three-Phase Current Source Inverter with Flexible PWM Capability

A new three-phase current source inverter topology is presented, consisting of three single-phase bridge inverters connected in series and feeding the isolated windings of a standard three-phase induction motor. Because a current zero in one phase now does not affect the others, it enables the implementation of a wide range of current PWM patterns for the reduction and selective elimination of torque pulsations. Furthermore, this system allows for very fast control of the fundamental load current through the use of sinusoidal PWM, a method that was not possible to implement on existing inverter topologies.

Efficient algorithms for computing Reeb graphs

The Reeb graph tracks topology changes in level sets of a scalar function and finds applications in scientific visualization and geometric modeling. We describe an algorithm that constructs the Reeb graph of a Morse function defined on a 3-manifold. Our algorithm maintains connected components of the two dimensional levels sets as a dynamic graph and constructs the Reeb graph in O(nlogn+nlogg(loglogg)3) time, where n is the number of triangles in the tetrahedral mesh representing the 3-manifold and g is the maximum genus over all level sets of the function. We extend this algorithm to construct Reeb graphs of d-manifolds in O(nlogn(loglogn)3) time, where n is the number of triangles in the simplicial complex that represents the d-manifold. Our result is a significant improvement over the previously known O(n2) algorithm. Finally, we present experimental results of our implementation and demonstrate that our algorithm for 3-manifolds performs efficiently in practice.