Dataset Information

Effective Augmentation of Complex Networks.

ABSTRACT: Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our "effective augmentation" algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. "Effective augmentation" is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value.

SUBMITTER: Wang J

PROVIDER: S-EPMC4863250 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Effective Augmentation of Complex Networks.

Wang Jinjian J Yu Xinghuo X Stone Lewi L

Scientific reports 20160511

Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., c ...[more]

PMID: 27165120

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Project description:Introduction: The lips and the mouth play an indispensable role in vocalization, mastication and face aesthetics. Various noxious factors may alter and destruct the original structure, and appearance of the lips and the anatomical area surrounding the mouth. The application of hyaluronic acid (HA) may serve as a safe method for lip regeneration. Although a number of studies exist for HA effectiveness and safety, its beneficial effect is not well-established. Aim: The present meta-analysis and systematic review was performed to investigate the effectiveness of HA on lip augmentation. We also investigated the types and nature of adverse effects (AEs) of HA application. Methods: We reported our meta-analysis in accordance with the PRISMA Statement. PROSPERO protocol registration: CRD42018102899. We performed the systematic literature search in CENTRAL, Embase, and MEDLINE. Randomized controlled trials, cohort studies, case series and case reports were included. The untransformed proportion (random-effects, DerSimonian-Laird method) of responder rate to HA injection was calculated. For treatment related AEs descriptive statistics were used. Results: The systematic literature search yielded 32 eligible records for descriptive statistics and 10 records for quantitative synthesis. The results indicated that the overall estimate of responders (percentage of subjects with increased lip fullness by one point or higher) was 91% (ES = 0.91, 95% CI:0.85-0.96) 2 months after injection. The rate of responders was 74% (ES = 0.74, 95% CI:0.66-0.82) and 46% (ES = 0.46, 95% CI:0.28-0.65) after 6 and 12 months, respectively. We included 1,496 participants for estimating the event rates of AEs. The most frequent treatment-related AEs were tenderness (88.8%), injection site swelling (74.3%) and bruising (39.5%). Rare AEs included foreign body granulomas (0.6%), herpes labialis (0.6%) and angioedema (0.3%). Conclusion: Our meta-analysis revealed that lip augmentation with injectable HA is an efficient method for increasing lip fullness for at least up to 6 months after augmentation. Moreover, we found that most AEs of HA treatment were mild or moderate, but a small number of serious adverse effects were also found. In conclusion, further well-designed RCTs are still needed to make the presently available evidence stronger.

Project description:RATIONALE AND OBJECTIVES:We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational efficiency while demonstrating accuracy and robustness. The large data requirement for the proposed framework is made possible by a novel template-based data augmentation strategy. Supporting this work is the open-source ANTsRNet-a growing repository of well-known deep learning architectures first introduced here. MATERIALS AND METHODS:Deep convolutional neural network (CNN) models were constructed and trained using a custom multilabel Dice metric loss function and a novel template-based data augmentation strategy. Training (including template generation and data augmentation) employed 205 proton MR images and 73 functional lung MRI. Evaluation was performed using data sets of size 63 and 40 images, respectively. RESULTS:Accuracy for CNN-based proton lung MRI segmentation (in terms of Dice overlap) was left lung: 0.93 ± 0.03, right lung: 0.94 ± 0.02, and whole lung: 0.94 ± 0.02. Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0.95 ± 0.02, right lung: 0.96 ± 0.01, and whole lung: 0.96 ± 0.01), processing time is <1 second per subject for the proposed approach versus ?30 minutes per subject using joint label fusion. Accuracy for quantifying ventilation defects was determined based on a consensus labeling where average accuracy (Dice multilabel overlap of ventilation defect regions plus normal region) was 0.94 for the CNN method; 0.92 for our previously reported method; and 0.90, 0.92, and 0.94 for expert readers. CONCLUSION:The proposed framework yields accurate automated quantification in near real time. CNNs drastically reduce processing time after offline model construction and demonstrate significant future potential for facilitating quantitative analysis of functional lung MRI.

Dataset Information

Effective Augmentation of Complex Networks.

Publications

Effective Augmentation of Complex Networks.

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