University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

Motile curved bacteria are Pareto-optimal

Abstract: Curved rods are a ubiquitous bacterial phenotype, but the fundamental question of why they are shaped this way remains unanswered. Through in silico experiments, we assessed freely swimming straight- and curved-rod bacteria of a wide diversity of equal-volume shapes parameterized by elongation and curvature, and predicted their performances in tasks likely to strongly influence overall fitness. Performance trade-offs between these tasks lead to a variety of shapes that are Pareto-optimal, including coccoids, all straight rods, and a range of curvatures. Comparison with an extensive morphological survey of motile curved-rod bacteria indicates that the vast majority of species fall within the Pareto-optimal region of morphospace. This result is consistent with evolutionary trade-offs between just three tasks: efficient swimming, chemotaxis, and low cell construction cost. We thus reveal the underlying selective pressures driving morphological diversity in a widespread component of microbial ecosystems.

Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI

Abstract: Purpose: Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time-consuming and subject to inter-operator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. Methods: Our fully automatic pipeline uniquely combines: (1) a multi-atlas based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (2) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground-truth segmentations in 37 patients with persistent long standing AF. Results: Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%) respectively compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91. Conclusion: Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localisation, visualisation and quantification of atrial scarring and to guide ablation treatment.

When should the chicken cross the road?: Game theory for autonomous vehicle-human interactions

Abstract: Autonomous vehicle control is well understood for local- [15], good approximations exist such as particle �ltering, ization, mapping and planning in un-reactive environ- which make use of large compute power to draw samples ments, but the human factors of complex interactions near solutions. stood [16], and despite its exact solution being NP-hard with other road users are not yet developed. Route planning in non-interactive envi- ronments also has well known tractable solutions such as This po- the A-star algorithm. Given a route, localizing and con- sition paper presents an initial model for negotiation be- trol to follow that route then becomes a similar task to tween an autonomous vehicle and another vehicle at an that performed by the 1959 General Motors Firebird-III unsigned intersections or (equivalently) with a pedestrian self-driving car [1], which used electromagnetic sensing at an unsigned road-crossing (jaywalking), using discrete to follow a wire built into the road. Such path follow- sequential game theory. The model is intended as a ba- ing, using wires or SLAM, can then be augmented with sic framework for more realistic and data-driven future simple safety logic to stop the vehicle if any obstacle is extensions. The model shows that when only vehicle po- in its way, as detected by any range sensor. sition is used to signal intent, the optimal behaviors for open source systems for this level of `self-driving' are now both agents must include a non-zero probability of al- widely available [6]. lowing a collision to occur. In contrast, This suggests extensions to problems that these vehicles will face around interacting with other road users are much harder reduce this probability in future, such as other forms of both to formulate and solve. Autonomous vehicles do not signaling and control. Unlike most Game Theory appli- just have to deal with inanimate objects, sensors, and cations in Economics, active vehicle control requires real- maps. time selection from multiple equilibria with no history, They have to deal with other agents, currently human drivers and pedestrians and eventually other au- and we present and argue for a novel solution concept, meta-strategy convergence , suited to this task.

Aircraft taxi time prediction: Comparisons and insights

Abstract: The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.

MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks

Abstract: In this paper, we propose a learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine-learned and hand-crafted features. Fully convolutional networks (FCN) forms the machine-learned features and texton based histograms are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors. The volumetric features from the segmented tumor tissues and patient age applying to an RF is used to predict the survival time. The method was evaluated on MICCAI-BRATS 2017 challenge dataset. The mean Dice overlap measures for segmentation of validation dataset are 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively. The validation Hausdorff values are 7.61, 8.70 and 3.76. For the survival prediction task, the classification accuracy, pairwise mean square error and Spearman rank are 0.485, 198749 and 0.334, respectively.