Cholera risk: A machine learning approach applied to essential climate variables

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems

Framework for Regional to Global Extension of Optical Water Types for Remote Sensing of Optically Complex Transitional Water Bodies

Water quality indicator algorithms often separate marine and freshwater systems, introducing artificial boundaries and artifacts in the freshwater to ocean continuum. Building upon the Ocean Colour- (OC) and Lakes Climate Change Initiative (CCI) projects, we propose an improved tool to assess the interactions across river–sea transition zones. Fuzzy clustering methods are used to generate optical water types (OWT) representing spectrally distinct water reflectance classes, occurring within a given region and period (here 2016–2021), which are then utilized to assign membership values to every OWT class for each pixel and seamlessly blend optimal in-water algorithms across the region. This allows a more flexible representation of water provinces across transition zones than classic hard clustering techniques. Improvements deal with expanded sensor spectral band-sets, such as Sentinel-3 OLCI, and increased spatial resolution with Sentinel-2 MSI high-resolution data. Regional clustering was found to be necessary to capture site-specific characteristics, and a method was developed to compare and merge regional cluster sets into a pan-regional representative OWT set. Fuzzy clustering OWT timeseries data allow unique insights into optical regime changes within a lagoon, estuary, or delta system, and can be used as a basis to improve WQ algorithm performance

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

National Museums Scotland, Digital Collecting in Museums, 2020

A multi-disciplinary group of museum and heritage professionals with shared interests in collecting born-digital material met at the National Museum of Scotland on 11 March 2020 to discuss best practice and opportunities. The symposium included a range of papers outlining different approaches to collecting and interpreting digital entities, with definite themes emerging from the symposium as a whole. Content ranged from photography to videogames, social media to digital-physical hybrid objects. Through engagement with digital objects, each of the speakers encountered similar opportunities and challenges. This report summarises the discussion along five interlinked themes: Defining the Digital Object, Methods, Display, Legal and Ethical Challenges and, finally, the overarching question ‘Why Collect the Born-Digital?’ Participants then contributed to quantitative and qualitative evaluation. Overall, a picture emerges of a growing (but by no means established) consensus on the methods, values and principles of digital collecting

Tilted X-Ray Holography of Magnetic Bubbles in MnNiGa Lamellae

Nanoscopic lamellae of centrosymmetric ferromagnetic alloys have recently been reported to host the biskyrmion spin texture; however, this has been disputed as the misidentication of topologically trivial type-II magnetic bubbles. Here we demonstrate resonant soft X-ray holographic imaging of topological magnetic states in lamellae of the centrosymmetric alloy (Mn1–xNix)0.65Ga0.35 (x = 0.5), showing the presence of magnetic stripes evolving into single core magnetic bubbles. We observe rotation of the stripe phase via the nucleation and destruction of disclination defects. This indicates the system behaves as a conventional uniaxial ferromagnet. By utilizing the holography with extended reference by autocorrelation linear dierential operator (HERALDO) method, we show tilted holographic images at 30° incidence confirming the presence of type-II magnetic bubbles in this system. This study demonstrates the utility of X-ray imaging techniques in identifying the topology of localized structures in nanoscale magnetism

Biodiversity from Remote Sensing of Coastal Areas for Science and Societal Applications: User Requirements Synthesis and Preliminary Results

The Convention on Biological Diversity and the UN Decade of the Ocean have set targets to reaching ocean sustainability by 2050. To assess if these targets have been met, each target is linked to a set of indicators measuring Essential Biodiversity Variables (EBV). Marine and coastal habitats are under threat through numerous anthropogenic stressors. At the same time, measuring indicators in the marine and coastal environment is costly, time consuming and unreliable due to weather conditions leading to a dearth of data in these areas. Satellite remote sensing is proposed as a tool to complement in-situ observations. It can measure some EBVs in a more consistent and reliable manner and increase the area covered as well as spatial and temporal resolution. However, due to the need for specific training and infrastructure to analyse raw remote sensing data, there is a need to understand the end users’ requirements to use such data for biodiversity monitoring. The European Space Agency funded Bi-COME project (Biodiversity of the Coastal Ocean: Monitoring with Earth Observation) aims to develop products that help measuring more EBVs more effectively and to involve stakeholders in the development process. To this end, we are collecting the user requirements of seven case study partners using semi-structured interviews. We aim to compare their current approaches with new Earth Observation products by learning about their current methods to measure EBVs, ask what they would like to achieve by the use of improved Earth Observation products and how they are able to access such data. The case study partners consist of managers and data providers to local environmental managers of intertidal, subtidal and pelagic marine habitats. The case study sites consist of sandy intertidal habitat in France, seagrass habitats in Mozambique and pelagic floating vegetation in India and the Caribbean Sea. We plan to interview the case study partners after they have tested the products created so that they can help shape the development according to their needs. This presentation will discuss results from the first set of interviews

Do Images of Biskyrmions Show Type-II Bubbles?

The intense research effort investigating magnetic skyrmions and their applications for spintronics has yielded reports of more exotic objects including the biskyrmion, which consists of a bound pair of counter‐rotating vortices of magnetization. Biskyrmions have been identified only from transmission electron microscopy images and have not been observed by other techniques, nor seen in simulations carried out under realistic conditions. Here, quantitative Lorentz transmission electron microscopy, X‐ray holography, and micromagnetic simulations are combined to search for biskyrmions in MnNiGa, a material in which they have been reported. Only type‐I and type‐II magnetic bubbles are found and images purported to show biskyrmions can be explained as type‐II bubbles viewed at an angle to their axes. It is not the magnetization but the magnetic flux density resulting from this object that forms the counter‐rotating vortices

Data set for: Real-space Imaging of Confined Magnetic Skyrmion Tubes

This repository contains the scripts and notebooks to reproduce the figures, simulations and numerical data shown in Real-space Imaging of Confined Magnetic Skyrmion Tubes by M. T. Birch, D. Cort&eacute;s-Ortu&ntilde;o, L. A. Turnbull, M. N. Wilson, F. Gro&szlig;, N. Tr&auml;ger, A. Laurenson, N. Bukin, S. H. Moody, M. Weigand, G. Sch&uuml;tz, H. Popescu, R. Fan, P. Steadman, J. A. T. Verezhak, G. Balakrishnan, J. C. Loudon, A. C. Twitchett-Harrison, O. Hovorka, H. Fangohr, F. Ogrin, J. Gr&auml;fe and P. D. Hatton. Both simulation and experimental data analysis are performed using Python with the Matplotlib, Jupyter, Scipy, Numpy and h5py libraries. Jupyter notebooks are provided to process the experimental data and reproduce the STXM, X-Ray Holography and LTEM images, which are shown as Figures 2, 3, 4 and 5 in the paper. Simulation scripts are based on the finite difference micromagnetic code OOMMF with the extension to simulate DMI for materials with symmetry class T: [oommf-extension-dmi-t](https://github.com/joommf/oommf-extension-dmi-t) The analysis of OOMMF&#39;s output files, which are in the `OMF` format, are processed using the [OOMMFPy](https://github.com/davidcortesortuno/oommfpy) library, which can calculate the topological charge in a 2D slice. Three-dimensional visualisations of the magnetic states are performed using Paraview. In order to get VTK files for visualisation, convert the `OMF` files into `.vtk` using the `OOMMFPy` library.</span