Classification and Segmentation of Blooms and Plumes from High Resolution Satellite Imagery Using Deep Learning

Recent launches of high-resolution satellite sensors mean Earth Observation data are available at an unprecedented spatial and temporal scale. As data collection intensifies, our ability to inspect and investigate individual scenes for harmful algal or cyanobacterial blooms becomes limited, particularly for global monitoring. Algal Blooms and River Plumes are visible to trained experts in high resolution satellite imagery from Red-Green-Blue composites. Therefore, computer-assisted detection and classification of these events would provide invaluable information to experts and the general public on everyday water use. Advances in image recognition through Deep Learning techniques offer solutions that can accurately detect, classify and segment objects across thousands of images in a fraction of the time a human operator would require, while inspecting these images with much greater detail. Deep Learning techniques that jointly leverage spectral-spatial data are well characterised as a solution to land classification problems and have been shown to be accurate when using multi- or hyper-spectral data such as collected by the Sentinel-2 MultiSpectral Instrument. This work develops on state-of-the-art natural image segmentation algorithms to evaluate the capability of Deep Learning to detect and outline the presence of Algal Blooms or River Plumes in Sentinel 2 MSI data. The challenges in the application of these approaches are highlighted in the availability of suitable training and benchmark data, the use of atmospheric correction and neural network architecture design for utilisation of spectral data. Current Deep Learning network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines. Finally a training dataset and associated Deep Learning method is presented for use in future work relating to water contents classification

Teaching computers to see from space: deep learning and Sentinel 2

An outline of progress in the first year of research activities under my PhD. This is an outline of how and why Deep Learning can be used with remote sensing data for water contents analysis and classification, results from proof of concept experiments are described and future research activities are explained. A recording of the presentation and associated questions is available at https://1drv.ms/v/s!AsHRpsQE0ig4jPcrly23In5Tbqd10

Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient

Different techniques exist for determining chlorophyll-a concentration as a proxy of phytoplankton abundance. In this study, a novel method based on the spectral particulate beam-attenuation coefficient (cp) was developed to estimate chlorophyll-a concentrations in oceanic waters. A multi-layer perceptron deep neural network was trained to exploit the spectral features present in cp around the chlorophyll a absorption peak in the red spectral region. Results show that the model was successful at accurately retrieving chlorophyll-a concentrations using cp in three red spectral bands,irrespective of time or location and over a wide range of chlorophyll-a concentrations

Deep Learning For Feature Tracking In Optically Complex Waters

PosterEnvironmental monitoring and early warning of water quality from space is now feasible at unprecedented spatial and temporal resolution following the latest generation of satellite sensors. The transformation of this data through classification into labelled, tracked event information is of critical importance to offer a searchable dataset. Advances in image recognition techniques through Deep Learning research have been successfully applied to satellite remote sensing data. Deep Learning approaches that leverage optical satellite data are now being developed for remotely sensed multi- and hyperspectral reflectance. The combination of spectral with spatial feature extracting Deep Learning networks promises a significant improvement in the accuracy of classifiers using remotely sensed data. This project aims to re-tool and optimise spectral-spatial Convolutional Neural Networks originally developed for land classification as a novel approach to identifying and labelling dynamic features in waterbodies, such as algal blooms and sediment plumes in high-resolution satellite sensors

The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2

There is growing experimental evidence that many respiratory viruses—including influenza and SARS-CoV-2—can interact, such that their epidemiological dynamics may not be independent. To assess these interactions, standard statistical tests of independence suggest that the prevalence ratio—defined as the ratio of co-infection prevalence to the product of single-infection prevalences—should equal unity for non-interacting pathogens. As a result, earlier epidemiological studies aimed to estimate the prevalence ratio from co-detection prevalence data, under the assumption that deviations from unity implied interaction. To examine the validity of this assumption, we designed a simulation study that built on a broadly applicable epidemiological model of co-circulation of two emerging or seasonal respiratory viruses. By focusing on the pair influenza–SARS-CoV-2, we first demonstrate that the prevalence ratio systematically underestimates the strength of interaction, and can even misclassify antagonistic or synergistic interactions that persist after clearance of infection. In a global sensitivity analysis, we further identify properties of viral infection—such as a high reproduction number or a short infectious period—that blur the interaction inferred from the prevalence ratio. Altogether, our results suggest that ecological or epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses

Phosphorus dynamics in the Barents Sea

The Barents Sea is considered a warming hotspot in the Arctic; elevated sea surface temperatures have been accompanied with increased inflow of Atlantic water onto the shelf sea. Such hydrodynamic changes and a concomitant reduction of sea ice coverage enables a prolonged phytoplankton growing season, which will inevitably affect nutrient stoichiometry and the controls on primary production. During the summer of 2018, we investigated the role of phosphorus in mediating primary production in the Barents Sea. Dissolved inorganic phosphorus (DIP), its most bioavailable form, had an average net turnover time of 9.4�4.8 d. The most southern Atlantic influenced station accounted for both the highest rates of primary production (655 mg C m2 d−1) and shortest net DIP turnover (2.8�0.5 d). The fraction of assimilated DIP released as dissolved organic phosphorus (DOP) at this station was < 4% compared to an average of 21% at all other stations. We observed significant differences between phytoplankton communities in Arctic and Atlantic waters within the Barents Sea. Slower DIP turnover and greater release of DOP was associated with Phaeocystis pouchetii dominated communities in Arctic waters. Faster turnover rates and greater phosphorus retention occurred among the Atlantic phytoplankton communities dominated by Emiliania huxleyi. Thesefindings provide baseline measurements of P utilization in the Barents Sea, and suggest increased Atlantic intrusion of this region could be accompanied by more rapid DIP turnover, possibly leading to future P limitation (rather than N limitation) on primary productio

Mechanical activation of vinculin binding to talin locks talin in an unfolded conformation

The force-dependent interaction between talin and vinculin plays a crucial role in the initiation and growth of focal adhesions. Here we use magnetic tweezers to characterise the mechano-sensitive compact N-terminal region of the talin rod, and show that the three helical bundles R1-R3 in this region unfold in three distinct steps consistent with the domains unfolding independently. Mechanical stretching of talin R1-R3 enhances its binding to vinculin and vinculin binding inhibits talin refolding after force is released. Mutations that stabilize R3 identify it as the initial mechano-sensing domain in talin, unfolding at ~5 pN, suggesting that 5 pN is the force threshold for vinculin binding and adhesion progression

Analysis of non-pharmaceutical interventions and their impacts on COVID-19 in Kerala

In the absence of an effective vaccine or drug therapy, non-pharmaceutical interventions are the only option for control of the outbreak of the coronavirus disease 2019, a pandemic with global implications. Each of the over 200 countries affected has followed its own path in dealing with the crisis, making it difficult to evaluate the effectiveness of measures implemented, either individually, or collectively. In this paper we analyse the case of the south Indian state of Kerala, which received much attention in the international media for its actions in containing the spread of the disease in the early months of the pandemic, but later succumbed to a second wave. We use a model to study the trajectory of the disease in the state during the first four months of the outbreak. We then use the model for a retrospective analysis of measures taken to combat the spread of the disease, to evaluate their impact. Because of the differences in the trajectory of the outbreak in Kerala, we argue that it is a model worthy of a place in the discussion on how the world might best handle this and other, future, pandemics

Force-dependent focal adhesion assembly and disassembly: A computational study

Cells interact with the extracellular matrix (ECM) via cell–ECM adhesions. These physical interactions are transduced into biochemical signals inside the cell which influence cell behaviour. Although cell–ECM interactions have been studied extensively, it is not completely understood how immature (nascent) adhesions develop into mature (focal) adhesions and how mechanical forces influence this process. Given the small size, dynamic nature and short lifetimes of nascent adhesions, studying them using conventional microscopic and experimental techniques is challenging. Computational modelling provides a valuable resource for simulating and exploring various “what if?” scenarios in silico and identifying key molecular components and mechanisms for further investigation. Here, we present a simplified mechano-chemical model based on ordinary differential equations with three major proteins involved in adhesions: integrins, talin and vinculin. Additionally, we incorporate a hypothetical signal molecule that influences adhesion (dis)assembly rates. We find that assembly and disassembly rates need to vary dynamically to limit maturation of nascent adhesions. The model predicts biphasic variation of actin retrograde velocity and maturation fraction with substrate stiffness, with maturation fractions between 18–35%, optimal stiffness of ∼1 pN/nm, and a mechanosensitive range of 1-100 pN/nm, all corresponding to key experimental findings. Sensitivity analyses show robustness of outcomes to small changes in parameter values, allowing model tuning to reflect specific cell types and signaling cascades. The model proposes that signal-dependent disassembly rate variations play an underappreciated role in maturation fraction regulation, which should be investigated further. We also provide predictions on the changes in traction force generation under increased/decreased vinculin concentrations, complementing previous vinculin overexpression/knockout experiments in different cell types. In summary, this work proposes a model framework to robustly simulate the mechanochemical processes underlying adhesion maturation and maintenance, thereby enhancing our fundamental knowledge of cell–ECM interactions

A Novel Mechanism for Calmodulin Dependent Inactivation of Transient Receptor Potential Vanilloid 6

The paralogues TRPV5 and TRPV6 belong to the vanilloid subfamily of the Transient Receptor Potential (TRP) superfamily of ion channels and both play an important role in overall Cahomeostasis. The functioning of the channels centres on a tightly controlled Ca-dependent feedback mechanism where the direct binding of the universal Ca-binding protein calmodulin (CaM) to the channel's C-terminal tail is required for channel inactivation. We have investigated this interaction at the atomic level and propose that under basal cellular [CaCaM is constitutively bound to the channel's C-tail via CaM C-lobe only contacts. When cytosolic [Ca] increases charging the apo CaM N-lobe with Ca, the CaM:TRPV6 complex rearranges and the TRPV6 C-tail further engages the CaM N-lobe via a crucial interaction involving L707. In a cellular context, mutation of L707 significantly increased the rate of channel inactivation. Finally, we present a model for TRPV6 CaM-dependent inactivation, which involves a novel so-called "two-tail" mechanism whereby CaM bridges between two TRPV6 monomers resulting in closure of the channel pore