Search

Motivation: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions., Results: Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni's L-measure. Coifman's harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances., Availability and Implementation: Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors, Contact: broysam@central.uh.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The dynamic nature of vehicular networks imposes a lot of challenges in multihop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. Thus, packets frequently find it difficult to get through to the destination due to the limited lifetimes of links. The conventional broadcasting based vehicular ad-hoc network (VANET) routing protocols struggle to accurately analyze the link dynamicity due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) paradigm, link stability can be better scrutinized pertaining to the availability of global network information. Thus, in this paper, we introduce an optimization based novel packet routing scheme with a source routing based flow instantiation (FI) operation for SDVN. The routing framework closely analyzes the stability of links in selecting the routes and the problem is formulated as a minimum cost capacitated flow problem. Furthermore, an incremental packet allocation scheme is proposed to solve the routing problem in a less time complexity. The objective is to find multiple shortest paths which are collectively stable enough to deliver a given number of packets. The FI scheme efficiently delivers and caches flow information in the required nodes with a reduced extent of communication with the control plane. With the help of realistic simulation, we show that the proposed routing framework excels in terms of the performance over the existing routing schemes of both SDVN and conventional VANET. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the performance comparison between flat and cluster-based routings in MANETs. Previous study shows that an earlier proposed cluster-based routing, Cluster based Routing Protocol (CBRP), has poorer performance due to its inefficient clustering algorithms based on Least Clusterhead Change (LCC) and Lowest ID (LID). Thus, in this paper, we first propose a new cluster-based routing, namely efficient cluster based routing protocol (ECBRP), which is based on a more efficient and stable clustering scheme. In addition, a new information table update mechanism utilizing routing events and data forwarding events is introduced in ECBRP to enhance the clustering and routing performance. The performance comparison in terms of packet delivery ratio and normalized routing overhead between flat and cluster based routings are studied extensively. Two flat routings (Dynamic Source Routing (DSR) and Ad Hoc On-demand Distance Vector (AODV)) and two cluster-based routing (CBRP and ECBRP) under various scenarios such as traffic load, mobility, network size and network area are considered in our study. By evaluating the performance of ECBRP and CBRP, we can study how the performance of a CBRP is affected by its underlying cluster structure. Simulation results show that ECBRP can achieve satisfying routing performance and outperform other routing schemes, DSR, AODV and CBRP, under the same simulated scenarios. [ABSTRACT FROM AUTHOR]

With the booming development of high-speed railways (HSRs), key technologies of wireless communications need to be constantly innovated. In particular, the frontier issue of low delay of the handover for the fifth generation (5G) in fast-moving scenarios has attracted attention from both industry and academia. Based on an analysis of a large number of measured data and the location of the user equipment (UE), a fast handover algorithm is proposed to solve the problem of long delay for a train moving at high speed in a 5G-railway (5G-R). By calculating the speed of a train and its direction of movement, a reasonable handover mode is selected and the handover chain of neighboring cells is identified. The location of the train can be calculated to determine whether UE enters the defined identification zone of pre-handover. Depending on the values collected in the measurement report, the command of the handover is triggered when the weight of the target cell is greater than that of the source cell. Our experimental results show that the delay of the fast handover algorithm is reduced by 2.03%, and the success rate of the handover is increased by 0.42%. Research directions for smart railways are discussed based on these findings. [ABSTRACT FROM AUTHOR]

A combined edge and core cloud computing environment is a novel solution in 5G network slices. The clients' high availability requirement is a challenge because it limits the possible admission control in front of the edge cloud. This work proposes an orchestrator with a mathematical programming model in a global viewpoint to solve resource management problems and satisfying the clients' high availability requirements. The proposed Lagrangian relaxation-based approach is adopted to solve the problems at a near-optimal level for increasing the system revenue. A promising and straightforward resource management approach and several experimental cases are used to evaluate the efficiency and effectiveness. Preliminary results are presented as performance evaluations to verify the proposed approach's suitability for edge and core cloud computing environments. The proposed orchestrator significantly enables the network slicing services and efficiently enhances the clients' satisfaction of high availability. [ABSTRACT FROM AUTHOR]