Intact Bilateral Resting-State Networks in the Absence of the Corpus Callosum

Temporal correlations between different brain regions in the resting-state BOLD signal are thought to reflect intrinsic functional brain connectivity (Biswal et al., 1995; Greicius et al., 2003; Fox et al., 2007). The functional networks identified are typically bilaterally distributed across the cerebral hemispheres, show similarity to known white matter connections (Greicius et al., 2009), and are seen even in anesthetized monkeys (Vincent et al., 2007). Yet it remains unclear how they arise. Here we tested two distinct possibilities: (1) functional networks arise largely from structural connectivity constraints, and generally require direct interactions between functionally coupled regions mediated by white-matter tracts; and (2) functional networks emerge flexibly with the development of normal cognition and behavior and can be realized in multiple structural architectures. We conducted resting-state fMRI in eight adult humans with complete agenesis of the corpus callosum (AgCC) and normal intelligence, and compared their data to those from eight healthy matched controls. We performed three main analyses: anatomical region-of-interest-based correlations to test homotopic functional connectivity, independent component analysis (ICA) to reveal functional networks with a data-driven approach, and ICA-based interhemispheric correlation analysis. Both groups showed equivalently strong homotopic BOLD correlation. Surprisingly, almost all of the group-level independent components identified in controls were observed in AgCC and were predominantly bilaterally symmetric. The results argue that a normal complement of resting-state networks and intact functional coupling between the hemispheres can emerge in the absence of the corpus callosum, favoring the second over the first possibility listed above

An agent based approach for improvised explosive device detection, public alertness and safety

One of the security challenges faced by our contemporary world is terror threats and attacks, and this is no doubt posing potential threats to lives, properties and businesses all around us; affecting the way we live and also travel. Terror attacks have been perpetrated in diverse ways whether from organized terror networks through coordinated attacks or by some lone individuals such that it is now a major concern to people and government. Indeed, there are numerous forms of terror attacks. In this proposal, we look at how the explosive substance kind of threats can be perceived and taken care of prior to potential attacks using intelligent agent systems requirement analysis. Thus, the paper demonstrates using an agent-oriented system analysis and design methodology to decompose. Through defined percepts, goals and plans, agents possess capabilities to observe and perform actions. This proposal demonstrates: how agents can be situated in our cities, goal refinement for agents in the detection and rescue of potential terror attacks, and inter-agent communication for the prevention of chemical terror attack

Synthesis and characterisation of an N-heterocyclic carbene with spatially-defined steric impact

The synthesis and co-ordination chemistry of a new ‘bulky yet flexible’ N-heterocyclic carbene (“IPaul”) is reported. This carbene has spatially-defined steric impact; steric maps show that two quadrants are very bulky while the other two are quite open. The electronic properties of this carbene are very similar to those of other 1,3-diarylimidazol-2-ylidenes. Copper, silver, iridium, and nickel complexes of the new ligand have been prepared. In solution, the ligand adopts two different conformations, while X-ray crystallographic analyses of the transition metal complexes suggest that the syn-conformer is preferred in the solid state due to intermolecular interactions. The copper complex of this new ligand has been shown to be highly-active in the hydrosilylation of carbonyl compounds, when compared to the analogous IPr, IMes, IPr* and IPr*OMe complexes

Relational Autoencoder for Feature Extraction

Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.Comment: IJCNN-201