Kronian Magnetospheric Reconnection Statistics Across Cassini's Lifetime

Magnetic reconnection is a fundamental physical process in planetary magnetospheres, in which plasma can be exchanged between the solar wind and a planetary magnetosphere, and material can be disconnected and ultimately lost from a magnetosphere. Magnetic reconnection in a planetary magnetotail can result in the release of plasmoids downtail and dipolarizations planetward of an x-line. The signatures of these products include characteristic deflections in the north-south component of the magnetic field which can be detected by in-situ spacecraft. These signatures have been identified by eye, semi-automated algorithms, and recently machine learning methods. Here, we apply statistical analysis to the most thorough catalogue of Kronian magnetospheric reconnection signatures created through machine learning methods to improve understanding of magnetospheric evolution. This research concludes that no quasi-steady position of the magnetotail x-line exists within 70 RS. This research introduces prediction equations to estimate the distribution of duration of plasmoid passage over the spacecraft (N = 300∆t −1.3 , bin width = 1 min) and north-south field deflection (N = 52∆B −2.1 θ , bin width = 0.25 nT) expected to be identified by an orbiting spacecraft across a year of observations. Furthermore, this research finds a local time asymmetry for reconnection identifications, with a preference for dusk-side over dawn-side. This may indicate a preference for Vasyliunas style reconnection over Dungey style for Saturn. Finally, through these distributions, the reconnection rate of Saturn’s magnetotail can be estimated as 3.22 reconnection events per day, with a resulting maximum mass loss from plasmoids of 44.87 kg s−1 on average, which is comparable with the magnetospheric mass loading from Enceladus (8-250 kg s−1 )

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

Space weather represents a severe threat to ground-based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post-processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near-Real-Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post-processed data, however there are three main areas of concern: greater short-term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably

Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10−8) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.This research has been conducted using the UK Biobank Resource. The Fenland Study is supported by the UK Medical Research Council (MRC) (MC_UU_12015/1; MC_UU_12015/2; MC_UU_12015/3). EPIC-Norfolk is supported by the MRC (G401527, G1000143) and Cancer Research UK (A8257). The HCS is gratefully supported by the University of Newcastle (Australia) and the Fairfax Family Foundation. Sydney MAS is supported by the Australian National Health and Medical Research Council (NHMRC), grants ID568969, ID350833 and ID109308. Sydney MAS DNA was extracted by Genetic Repositories Australia, funded by NHMRC Enabling Grant 401184. The GEFOS Study, used as controls for the US and Jamaican athletes, was supported in part by NIH grants U01 HG004436 and P30 DK072488, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). TwinsUK was funded by the Wellcome Trust (WT), MRC, and European Union. The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. SNP Genotyping was performed by The WT Sanger Institute and National Eye Institute via NIH/CIDR. M.McC is a WT Senior Investigator and receives support from WT 090532 and 098381. TW is the recipient of a studentship from MedImmune. Research by A. Lucia is supported by Fondo de Investigaciones Sanitarias and Fondos Feder (grant # PI15/0558). EM-M. was a recipient of a Grant-in-Aid for JSPS Fellow from the Japan Society for the Promotion of Science. This work was supported in part by grants from the Grant-in-Aid for Scientific Research (B) (15H03081 to NF) of the Japanese Ministry of Education, Culture, Sports, Science and Technology and by a grant-in-aid for scientific research (to M. Miyachi) from the Japanese Ministry of Health, Labor, and Welfare. This work was further supported by NIH grants R01 AR41398 and U24 AG051129

On the considerations of using near real time data for space weather hazard forecasting

Space weather represents a severe threat to ground-based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post-processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near-Real-Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post-processed data, however there are three main areas of concern: greater short-term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably

A Community Dataset for Comparing Automated Coronal Hole Detection Schemes

This is the author accepted manuscript.Automated detection schemes are nowadays the standard approach for locating coronal holes in EUV images from the Solar Dynamics Observatory (SDO). But factors such as the noisy nature of solar imagery, instrumental effects, and others make it challenging to identify coronal holes using these automated schemes. While discrepancies between detection schemes have been noted in the literature, a comprehensive assessment of these discrepancies is still lacking. The contribution of the Coronal Hole Boundary Working Team in the COSPAR ISWAT initiative is threefold to close this gap. First, we present the first community dataset for comparing automated coronal hole detection schemes. This dataset consists of 29 SDO images, all of which were selected by experienced observers to challenge automated schemes. Second, we use this community dataset as input to 14 widely-applied automated schemes to study coronal holes and collect their detection results. Third, we study three SDO images from the dataset that exemplify the most important lessons learned from this effort. Our findings show that the choice of the automated detection scheme can have a significant effect on the physical properties of coronal holes, and we discuss the implications of these findings for open questions in solar and heliospheric physics. We envision that this community dataset will serve the scientific community as a benchmark dataset for future developments in the field.Austrian Science Fund (FWF)European Research Council (ERC)NAS

Bacterial and host determinants of cough aerosol culture positivity in patients with drug-resistant versus drug-susceptible tuberculosis

A burgeoning epidemic of drug-resistant tuberculosis threatens to derail global control efforts. Although the mechanisms remain poorly clarified, drug-resistant strains are widely believed to be less infectious than drug-susceptible strains. Consequently, we hypothesised that lower proportions of drug-resistant TB patients would have culturable Mycobacterium tuberculosis from respirable cough-generated aerosols compared to drug-susceptible TB patients, and that multiple factors, including mycobacterial genomic variation, would predict culturable cough aerosol production. We enumerated colony forming units (CFU) in aerosols (≤10μm) from 500 tuberculosis patients (227 with drug-resistance), compared clinical characteristics, and performed mycobacterial whole genome sequencing, dormancy phenotyping, and drug susceptibility analyses on M. tuberculosis from sputum. After considering treatment duration, we found that almost half of drug-resistant tuberculosis patients were cough aerosol culture-positive. Surprisingly, neither mycobacterial genomic variants, lineage, nor dormancy status predicted cough aerosol culture-positivity. However, mycobacterial sputum bacillary load and clinical characteristics, including a lower symptom score and stronger cough, were strongly predictive; thereby supporting targeted transmission-limiting interventions. Effective treatment largely abrogated cough aerosol culture-positivity, however, this was not always rapid. These data question current paradigms, inform public health strategies, and suggest the need to redirect tuberculosis transmission-associated research efforts towards host-pathogen interactions

Upregulation of ADAM-17 expression in active lesions in multiple sclerosis

ADAM-17, a disintegrin and metalloproteinase, is the major proteinase responsible for the cleavage of membrane-bound tumour necrosis factor (TNF) as well as being an active sheddase of other cytokines, cytokine receptors, growth factors and adhesion molecules. TNF is a major proinflammatory cytokine that has been identified as having a pathogenic role in inflammatory diseases within the CNS including multiple sclerosis (MS). Here we report the cellular origin and distribution of ADAM- 17 expression within clinically and neuropathologically confirmed MS and normal control white matter, assessed by immunohistochemistry, western blotting and PCR. ADAM-17 expression was associated with the blood vessel endothelium, activated macrophages/microglia and parenchymal astrocytes in MS white matter. Increased levels of ADAM-17 immunoreactivity were displayed in active lesions with evidence of recent myelin breakdown. Further studies into the functional role of ADAM-17 in the pathogenesis of MS and other inflammatory conditions are required.</p