Genetic markers in s. Paratyphi c reveal primary adaptation to pigs

Salmonella enterica with the identical antigenic formula 6,7:c:1,5 can be differentiated biochemically and by disease syndrome. One grouping, Salmonella Paratyphi C, is currently considered a typhoidal serovar, responsible for enteric fever in humans. The human-restricted typhoidal serovars (S. Typhi and Paratyphi A, B and C) typically display high levels of genome degradation and are cited as an example of convergent evolution for host adaptation in humans. However, S. Paratyphi C presents a different clinical picture to S. Typhi/Paratyphi A, in a patient group with predisposition, raising the possibility that its natural history is different, and that infection is invasive salmonellosis rather than enteric fever. Using whole genome sequencing and metabolic pathway analysis, we compared the genomes of 17 S. Paratyphi C strains to other members of the 6,7:c:1,5 group and to two typhoidal serovars: S. Typhi and Paratyphi A. The genome degradation observed in S. Paratyphi C was much lower than S. Typhi/Paratyphi A, but similar to the other 6,7:c:1,5 strains. Genomic and metabolic comparisons revealed little to no overlap between S. Paratyphi C and the other typhoidal serovars, arguing against convergent evolution and instead providing evidence of a primary adaptation to pigs in accordance with the 6,7:c:1.5 strains

Extremes in worldwide geomagnetic activity

Geomagnetic storms pose a hazard to many modern technologies. Therefore understanding how severe such storms could be is important to a wide range of space weather data and forecast end-users. Extreme value statistical (EVS) methods are therefore applied to a global set of geomagnetic observatory data to determine the one in 100 and one in 200 year extreme values in the north, east and horizontal field strengths and their time rates-of-change. We use one-minute digital data from geographically widely distributed observatories with typically a few decades of digital operations. Individual generalised Pareto distribution functions are fitted to the tail of each observatory data distribution, above some threshold marking the onset of extreme activity for that location. We discuss the return levels, for the one in 100 and one in 200 year events, with respect to the geographical distribution of the observatories, the proximity to auroral and equatorial electrojets and compare results with a separate EVS study of European-only magnetic observatory dat

An investigation of the role of microglia in axonal damage, in the context of demyelination

Axonal loss is the main determinant of permanent neurological disability in the demyelinating disease, multiple sclerosis. However, there remains uncertainty regarding the nature of the cellular factors that elicit axonal injury. Using a non-immune mediated model of demyelination, the Plp1-overexpressing mouse (line #72), it has been shown that early axonal changes occur most frequently in regions of active demyelination where microglia/macrophages are phagocytosing myelin debris (Edgar et al.2010). With this in mind, it seems reasonable to hypothesise that myelin-laden microglia/macrophages are axono-toxic. However, the literature is contradictory, with both pro- (Williams et al., 1994;Mosley and Cuzner, 1996;van der Laan et al., 1996) and anti-inflammatory (Boven et al., 2006;Liu et al., 2006) properties having been ascribed to this population. The aims of this study were therefore to determine, using an in vitro myelinating culture system as a model of CNS white matter, whether myelin-laden microglia/macrophages acquire a (i) pro-inflammatory/axono-toxic or (ii) anti-inflammatory/axono-protective phenotype. Myelinating cultures were prepared from embryonic mice expressing cyan fluorescent protein under the Thy1 promoter, rendering a subset of neurons fluorescent. Myelin-enriched tissue fractions, to mimic myelin debris, were prepared from wild type or Plp1-overexpressing adult mouse spinal cord and labelled with a rhodamine antibody labelling kit. Myelin fractions were added directly to myelinating cultures at 20, 24 or 27 days in vitro (DIV), for 7, 3 or 1 day(s), respectively. Rhodamine-labelled myelin was phagocytosed by CD45 positive microglia/macrophages within 24 hours of its addition to the cell cultures. The phagocytosis of myelin had no effect on CD45 positive cell density, size or morphology, even after incubation for up to 7 days. Densities of CFP-positive neurites were quantified and the proportion of CFP-positive neurites manifesting signs of injury were calculated. There was no evidence that myelin-laden microglia/macrophages could elicit axonal injury in vitro, with no changes in neuritic density or integrity observed in myelin treated cultures compared to controls. To determine if myelin-laden cells exert an anti-inflammatory, axono-protective effect in an inflammatory environment, cultures were treated with 100 ng/ml lipopolysaccharide (LPS). Pro-inflammatory cytokines, such as IL-1alpha, IL-1beta, IL-6 and TNF-alpha were up-regulated in the medium from the LPS-stimulated cultures. However, these levels were not altered when cultures were pre-treated with myelin debris, to generate myelin-laden microglia. In these LPS-stimulated cultures, neuritic densities and neuritic integrity remained unaltered, either in the presence or absence of myelin-laden microglia/macrophages, compared to controls. Therefore the putative axono-protective effect of these cells could not be determined

Evaluating the use of geomagnetic indices for identifying potential damage to power grids

Extreme geomagnetic storms have the potential to have a damaging impact on power infrastructure, through the currents they induce in the ground, termed geomagnetically induced currents (GICs). Such events are often classified and forecast in terms of the Kp index, with a Kp=9o event described as a G5 storm on the NOAA Space Weather Scale for geomagnetic activity. However, this global average, mid-latitude, 3-hour index is unlikely to be the most useful indicator of the short time-scale variations in magnetic field that are of most concern to power transmission operators. For example, depending on latitude an observatory K index of nine can represent a magnetic variation from tens to a few thousand nT. We therefore investigate the relation between GICs, both measured and modelled, to a range of geomagnetic indices. We use a range of validation techniques to evaluate how well these indices perform at identifying periods of large GICs in the UK, focusing in particular on geomagnetic storms. We also extend the study to look at possibilities for a new local index, based directly on the rate-of-change of the magnetic field, as well as forecasts of indices

Initial results of pipeline modelling in Great Britain

Large geomagnetic field variations during geomagnetic storms induce telluric currents in pipelines. These currents can lead to variations in the pipe-to-soil potentials (PSPs) which interfere with corrosion-prevention measures and may enhance corrosion, leading to localised damage and a reduced lifetime of the pipeline. Modelling PSP fluctuations is useful for mitigation measures in existing pipelines, as well as at the design stage to allow new pipelines to be built to withstand such impacts. We present a first attempt to build capability for modelling these currents in the high-pressure gas pipeline network of the United Kingdom. Our philosophy is similar to the approach used in the modelling of the UK high-voltage (HV) electrical network, as the pipeline network topology is somewhat similar to that of the HV network across much of Britain. We use the method of Boteler (GJI, 2013, doi: 10.1093/gji/ggs113) and modify our existing HV code to account for the continuous grounding of the pipelines, splitting each pipeline into straight-line segments and assuming a constant surface electric field within each section. We present some early results for simple pipeline models and simplified electric fields and we discuss the results

Validation of GIC in the GB High Voltage Network

The high-voltage (HV) power network of the mainland Great Britain (GB) consists of over 400 nodes and 750 connections. Using the National Grid Ten-Year Electricity Statement, we have developed an up-to-date model of the HV grid capturing the locations of the transformers, their connectivity and the overall topology of the network including double-circuits. We used the test network provided by Horton et al. (2012) to test our modelling methodology and to validate the code at the GIC calculation step using a uniform electric field. We then applied the same methodology to the GB grid and compared the output with a small set of GIC measurements available for the March 2015 and the September 2017 storm. We find reasonably good agreement between modelled and measured electric fields and GIC, giving us confidence that our models are providing sensible estimates of GIC at the sites where we have measurements. However, the GIC measurements are confined to a small region of the grid. As part of a wider programme, our next step will be to use the differential magnetometer method (DMM) to measure GIC under individual power lines across the UK over the next two years. We outline the approach that we will take to reconcile the flow of GIC within our model and the indirect measurements of GIC (as they will not be made at a transformer ground point). Ultimately, we wish to fully validate the HV grid model of GB (and as a complementary output the geoelectric field models) from the DMM measurements. This will allow future improvements such as transformer-level modelling and mitigation strategies to be tested

Probabilistic hazard assessment: application to geomagnetic activity

Probabilistic Hazard Assessment (PHA) provides an appropriate methodology for assessing space weather hazards and their impact on technology. PHA is widely used in geosciences to determine the probability of exceedance of critical thresholds, caused by one or more hazard sources. PHA has proved useful with limited historical data to estimate the likelihood of specific impacts. PHA has also driven the development of empirical and physical models, or ensembles of models, to replace measured data. Here we aim to highlight the PHA method to the space weather community and provide an example of how it could be used. In terms of space weather impact, the critical hazard thresholds might include the Geomagnetically Induced Current in a specific high voltage power transformer neutral, or the local pipe-to-soil potential in a particular metal pipe. We illustrate PHA in the space weather context by applying it to a twelve-year dataset of Earth-directed solar Coronal Mass Ejections (CME), which we relate to the probability that the global three-hourly geomagnetic activity index Kp exceeds specific thresholds. We call this a “Probabilistic Geomagnetic Hazard Assessment”, or PGHA. This provides a simple but concrete example of the method. We find that the cumulative probability of Kp > 6−, > 7−, > 8− and Kp = 9o is 0.359, 0.227, 0.090, 0.011, respectively, following observation of an Earth-directed CME, summed over all CME launch speeds and solar source locations. According to the historical Kp distribution, this represents an order of magnitude increase in the a priori probability of exceeding these thresholds. For the lower Kp thresholds, the results are somewhat distorted by our exclusion of coronal hole high-speed stream effects. The PHGA also reveals useful probabilistic associations between solar source location and subsequent maximum Kp for operational forecasters

Recent advances and validation of GIC modelling in the UK

We present a major upgrade to the power network model for the mainland UK and provide a validation of it with respect to both measured and synthetic data. Although we have only limited measured geomagnetically induced current (GIC) data with which to verify fully this updated model, we present an investigation of the sensitivity or accuracy of the model at each step in the modelling process: (1) The input geomagnetic field – we investigate the variability in modelled substation GIC with respect to network distance from magnetic field measurements made at magnetic observatories. (2) The electric field calculation - we use electric field measurements at the three UK observatories to test both the code and conductivity model used to compute the electric field across the UK. (3) The estimation of GICs in the power network - we use the test network provided by Horton et al. (2012), to test our modelling methodology and to validate the code at the GIC calculation step using a uniform electric field. (4) As a final step, we compare the output from the complete modelling chain with a small set of GIC measurements available for the March 2015 storm. We find that magnetic field measurements from observatories within a few hundred kilometres of the network can be used to estimate GIC within 30-40% of the true value; whilst observatories that are further away are less reliable, underestimating the largest values and recording false extremes. We also find good agreement between modelled and measured electric fields and GIC, giving us confidence that our models are providing sensible estimates of GIC. This research has benefitted from support from Natural Environment Research Council grant number NE/P017231/1

From SWIGS to SWIMMR

Neil Rogers, Juliane Hübert, Gemma Richardson, and Alan Thomson report from a RAS meeting in March that considered how to understand the potential hazard to our technology and infrastructure from extreme solar activity