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

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

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

The Talin Head Domain Reinforces Integrin-Mediated Adhesion by Promoting Adhesion Complex Stability and Clustering

Talin serves an essential function during integrin-mediated adhesion in linking integrins to actin via the intracellular adhesion complex. In addition, the N-terminal head domain of talin regulates the affinity of integrins for their ECM-ligands, a process known as inside-out activation. We previously showed that in Drosophila, mutating the integrin binding site in the talin head domain resulted in weakened adhesion to the ECM. Intriguingly, subsequent studies showed that canonical inside-out activation of integrin might not take place in flies. Consistent with this, a mutation in talin that specifically blocks its ability to activate mammalian integrins does not significantly impinge on talin function during fly development. Here, we describe results suggesting that the talin head domain reinforces and stabilizes the integrin adhesion complex by promoting integrin clustering distinct from its ability to support inside-out activation. Specifically, we show that an allele of talin containing a mutation that disrupts intramolecular interactions within the talin head attenuates the assembly and reinforcement of the integrin adhesion complex. Importantly, we provide evidence that this mutation blocks integrin clustering in vivo. We propose that the talin head domain is essential for regulating integrin avidity in Drosophila and that this is crucial for integrin-mediated adhesion during animal development

The Mechanical Basis of Memory – the MeshCODE Theory

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix (ECM) adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer

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

Structure calculation, refinement and validation using CcpNmr Analysis

CcpNmr Analysis provides a streamlined pipeline for both NMR chemical shift assignment and structure determination of biological macromolecules. In addition, it encompasses tools to analyse the many additional experiments that make NMR such a pivotal technique for research into complex biological questions. This report describes how CcpNmr Analysis can seamlessly link together all of the tasks in the NMR structure-determination process. It details each of the stages from generating NMR restraints [distance, dihedral,hydrogen bonds and residual dipolar couplings (RDCs)],exporting these to and subsequently re-importing them from structure-calculation software (such as the programs CYANA or ARIA) and analysing and validating the results obtained from the structure calculation to, ultimately, the streamlined deposition of the completed assignments and the refined ensemble of structures into the PDBe repository. Until recently, such solution-structure determination by NMR has been quite a laborious task, requiring multiple stages and programs. However, with the new enhancements to CcpNmr Analysis described here, this process is now much more intuitive and efficient and less error-prone