Efficient Dynamic Approximate Distance Oracles for Vertex-Labeled Planar Graphs

Let $G$ be a graph where each vertex is associated with a label. A Vertex-Labeled Approximate Distance Oracle is a data structure that, given a vertex $v$ and a label $\lambda$, returns a $(1+\varepsilon)$-approximation of the distance from $v$ to the closest vertex with label $\lambda$ in $G$. Such an oracle is dynamic if it also supports label changes. In this paper we present three different dynamic approximate vertex-labeled distance oracles for planar graphs, all with polylogarithmic query and update times, and nearly linear space requirements

GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd

Enabling internal electronic circuitry within additively manufactured metal structures - The effect and importance of inter-laminar topography

Purpose: This paper aims to explore the potential of ultrasonic additive manufacturing (UAM) to incorporate the direct printing of electrical materials and arrangements (conductors and insulators) at the interlaminar interface of parts during manufacture to allow the integration of functional and optimal electrical circuitries inside dense metallic objects without detrimental effect on the overall mechanical integrity. This holds promise to release transformative device functionality and applications of smart metallic devices and products. Design/methodology/approach: To ensure the proper electrical insulation between the printed conductors and metal matrices, an insulation layer with sufficient thickness is required to accommodate the rough interlaminar surface which is inherent to the UAM process. This in turn increases the total thickness of printed circuitries and thereby adversely affects the integrity of the UAM part. A specific solution is proposed to optimise the rough interlaminar surface through deforming the UAM substrates via sonotrode rolling or UAM processing. Findings: The surface roughness (Sa) could be reduced from 4.5 to 4.1 µm by sonotrode rolling and from 4.5 to 0.8 µm by ultrasonic deformation. Peel testing demonstrated that sonotrode-rolled substrates could maintain their mechanical strength, while the performance of UAM-deformed substrates degraded under same welding conditions ( approximately 12 per cent reduction compared with undeformed substrates). This was attributed to the work hardening of deformation process which was identified via dual-beam focussed ion beam–scanning electron microscope investigation. Originality/value: The sonotrode rolling was identified as a viable methodology in allowing printed electrical circuitries in UAM. It enabled a decrease in the thickness of printed electrical circuitries by ca. 25 per cent

A statistical strategy to identify recombinant viral ribonucleoprotein of avian, human, and swine influenza A viruses with elevated polymerase activity

Objectives: Reassortment of influenza A viruses can give rise to viral ribonucleoproteins (vRNPs) with elevated polymerase activity and the previous three pandemic influenza viruses contained reassorted vRNPs of different origins. These suggest that reassorted vRNP may be one of the factors leading to a pandemic virus. In this study, we reconstituted chimeric vRNPs with three different viral strains isolated from avian, human and swine hosts. We applied a statistical strategy to identify the effect that the origin of a single vRNP protein subunit or the interactions between these subunits on polymerase activity. Design: Eighty one chimeric vRNPs were reconstituted in 293T cells at different temperatures. Polymerase activity was determined by luciferase reporter assay and the results were analysed by multiway anova and other statistical methods. Results: It was found that PB2, PB1, NP, PB2-PB1 interaction, PB2-PA interaction and PB1-NP interaction had significant effect on polymerase activity at 37°C and several single subunits and interactions were identified to lead to elevation of polymerase activity. Furthermore, we studied 27 out of these 81 different chimieric vRNPs in different combinations via fractional factorial design approach. Our results suggested that the approach can identify the major single subunit or interaction factors that affect the polymerase activity without the need to experimentally reproduce all possible vRNP combinations. Conclusions: Statistical approach and fractional factorial design are useful to identify the major single subunit or interaction factors that can modulate viral polymerase activity. © 2013 John Wiley & Sons Ltd.published_or_final_versio

Mechanisms of mechanical reinforcement by graphene and carbon nanotubes in polymer nanocomposites

Polymer nanocomposites reinforced with carbon-based nanofillers are gaining increasing interest for a number of applications due to their excellent properties. The understanding of the reinforcing mechanisms is, therefore, very important for the maximization of performance. This present review summarizes the current literature status on the mechanical properties of composites reinforced with graphene-related materials (GRMs) and carbon nanotubes (CNTs) and identifies the parameters that clearly affect the mechanical properties of the final materials. It is also shown how Raman spectroscopy can be utilized for the understanding of the stress transfer efficiency from the matrix to the reinforcement and it can even be used to map stress and strain in graphene. Importantly, it is demonstrated clearly that continuum micromechanics that was initially developed for fibre-reinforced composites is still applicable at the nanoscale for both GRMs and CNTs. Finally, current problems and future perspectives are discussed