On the transport mechanisms, ecological interactions and fate of microplastics in aquatic environments

Microplastics are now persistent throughout aquatic systems globally and can cause a range of ecological damage. The transport of microplastics is influenced by the polymer type, in addition to physical, biological, and chemical gradients plastic particles move through as they are transported across freshwater to marine environments. The combined influence of these mechanisms on microplastic fate is largely unquantified, which prevents identification of accumulation zones and the related development of effective mitigation measures. This research applies a multidisciplinary approach that combines innovative experiments and physical modelling with detailed fieldwork to quantify the main factors influencing microplastic transport and fate. Using a suite of novel settling experiments, biofouling is shown to be a principal factor affecting microplastic deposition through changes in specific density. Yet settling regimes differ depending on polymer and shape as well as ambient sediment and salinity concentrations. To understand particle distribution further, abundances and fluxes of microplastics within a large river system are coupled with hydrological data to explore how microplastics are transported through the vertical water column , finding that most are dispersed below the water surface, yet concentration is dependent on seasonal discharge. Finally, the role of complex coastal ecosystems as a sink for microplastics is investigated in a hydraulic flume under a range of flow conditions. It is shown, for the first time, that microplastic trapping efficiency could be high for both sparse and dense coral canopies due to a reduction in streamwise velocities causing settling on, within, and behind coral structures. It is concluded that although sediment laws can provide a basic understanding of microplastic transport, a new generation of microplastic transport regimes is needed. The findings from the thesis enhance our knowledge of the complex mechanisms that govern microplastic transport and fate in aquatic environments and this new understanding is contextualised in terms of their broad ecological implications

Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

Microplastic trapping efficiency and hydrodynamics in model coral reefs: A physical experimental investigation.

Coastal ecosystems, such as coral reefs, are vulnerable to microplastic pollution input from proximal riverine and shoreline sources. However, deposition, retention, and transport processes are largely unevaluated, especially in relation to hydrodynamics. For the first time, we experimentally investigate the retention of biofilmed microplastic by branching 3D printed corals (staghorn coral Acropora genus) under various unidirectional flows (U = {0.15, 0.20, 0.25, 0.30} ms ) and canopy densities (15 and 48 corals m ). These variables are found to drive trapping efficiency, with 79-98% of microplastics retained in coral canopies across the experimental duration at high flow velocities (U = 0.25-0.30 ms ), compared to 10-13% for the bare bed, with denser canopies retaining only 15% more microplastics than the sparse canopy at highest flow conditions (U = 0.30 ms ). Three fundamental trapping mechanisms were identified: (a) particle interception, (b) settlement on branches or within coral, and (c) accumulation in the downstream wake region of the coral. Corresponding hydrodynamics reveal that microplastic retention and spatial distribution is modulated by the energy-dissipative effects of corals due to flow-structure interactions reducing in-canopy velocities and generating localised turbulence. The wider ecological implications for coral systems are discussed in light of the findings, particularly in terms of concentrations and locations of plastic accumulation. [Abstract copyright: Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms

There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem as it makes training data heterogeneous but so far no attempts have been made to explicitly capture this variation. Here, we propose an approach capable of mimicking different styles of segmentation, which potentially can improve quality and clinical acceptance of automatic segmentation methods. In this work, instead of training one neural network on all available data, we train several neural networks on subgroups of data belonging to different segmentation variations separately. Because a priori it may be unclear what styles of segmentation exist in the data and because different styles do not necessarily map one-on-one to different observers, the subgroups should be automatically determined. We achieve this by searching for the best data partition with a genetic algorithm. Therefore, each network can learn a specific style of segmentation from grouped training data. We provide proof of principle results for open-sourced prostate segmentation MRI data with simulated observer variations. Our approach provides an improvement of up to 23% (depending on simulated variations) in terms of Dice and surface Dice coefficients compared to one network trained on all data.Comment: 11 pages, 5 figures, SPIE Medical Imaging Conference - 202

Non-buoyant microplastic settling velocity varies with biofilm growth and ambient water salinity

Rivers are the major conveyor of plastics to the marine environment, but the mechanisms that impact microplastic (<5 mm) aquatic transport, and thus govern fate are largely unknown. This prevents progress in understanding microplastic dynamics and identifying zones of high accumulation, along with taking representative environmental samples and developing effective mitigation measures. Using a suite of settling experiments we show that non-buoyant microplastic settling is influenced by a combination of biofilm growth, water salinity and suspended clay concentrations typically seen across fluvial to marine environments. Results indicate that biofilms significantly increased settling velocity of three different polymer types of non-buoyant microplastics (fragments and fibres, size range 0.02–4.94 mm) by up to 130% and significant increases in settling velocity were observable within hours. Impacts were both polymer and shape specific and settling regimes differed according to both salinity and sediment concentrations. Our results further validate previous statements that existing transport formula are inadequate to capture microplastic settling and highlight the importance of considering the combination of these processes within the next generation of predictive frameworks. This will allow more robust predictions of transport, fate and impact of microplastic pollution within aquatic environments