Synthesis of 3-D coronal-solar wind energetic particle acceleration modules

1. Introduction Acute space radiation hazards pose one of the most serious risks to future human and robotic exploration. Large solar energetic particle (SEP) events are dangerous to astronauts and equipment. The ability to predict when and where large SEPs will occur is necessary in order to mitigate their hazards. The Coronal-Solar Wind Energetic Particle Acceleration (C-SWEPA) modeling effort in the NASA/NSF Space Weather Modeling Collaborative [Schunk, 2014] combines two successful Living With a Star (LWS) (http://lws. gsfc.nasa.gov/) strategic capabilities: the Earth-Moon-Mars Radiation Environment Modules (EMMREM) [Schwadron et al., 2010] that describe energetic particles and their effects, with the Next Generation Model for the Corona and Solar Wind developed by the Predictive Science, Inc. (PSI) group. The goal of the C-SWEPA effort is to develop a coupled model that describes the conditions of the corona, solar wind, coronal mass ejections (CMEs) and associated shocks, particle acceleration, and propagation via physics-based modules. Assessing the threat of SEPs is a difficult problem. The largest SEPs typically arise in conjunction with X class flares and very fast (\u3e1000 km/s) CMEs. These events are usually associated with complex sunspot groups (also known as active regions) that harbor strong, stressed magnetic fields. Highly energetic protons generated in these events travel near the speed of light and can arrive at Earth minutes after the eruptive event. The generation of these particles is, in turn, believed to be primarily associated with the shock wave formed very low in the corona by the passage of the CME (injection of particles from the flare site may also play a role). Whether these particles actually reach Earth (or any other point) depends on their transport in the interplanetary magnetic field and their magnetic connection to the shock

Behavioural responses to SARS-CoV-2 antibody testing in England: REACT-2 study

Background: This study assesses the behavioural responses to SARS-CoV-2 antibody test results as part of the REal-time Assessment of Community Transmission-2 (REACT-2) research programme, a large community-based surveillance study of antibody prevalence in England. Methods: A follow-up survey was conducted six weeks after the SARS-CoV-2 antibody test. The follow-up survey included 4500 people with a positive result and 4039 with a negative result. Reported changes in behaviour were assessed using difference-in-differences models. A nested interview study was conducted with 40 people to explore how they thought through their behavioural decisions. Results: While respondents reduced their protective behaviours over the six weeks, we did not find evidence that positive test results changed participant behaviour trajectories in relation to the number of contacts the respondents had, for leaving the house to go to work, or for leaving the house to socialise in a personal place. The qualitative findings supported these results. Most people did not think that they had changed their behaviours because of their test results, however they did allude to some changes in their attitudes and perceptions around risk, susceptibility, and potential severity of symptoms. Conclusions: We found limited evidence that knowing your antibody status leads to behaviour change in the context of a research study. While this finding should not be generalised to widespread self-testing in other contexts, it is reassuring given the importance of large prevalence studies, and the practicalities of doing these at scale using self-testing with lateral flow immunoassay (LFIA)

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

A Feasibility Study of Quantifying Longitudinal Brain Changes in Herpes Simplex Virus (HSV) Encephalitis Using Magnetic Resonance Imaging (MRI) and Stereology.

OBJECTIVES: To assess whether it is feasible to quantify acute change in temporal lobe volume and total oedema volumes in herpes simplex virus (HSV) encephalitis as a preliminary to a trial of corticosteroid therapy. METHODS: The study analysed serially acquired magnetic resonance images (MRI), of patients with acute HSV encephalitis who had neuroimaging repeated within four weeks of the first scan. We performed volumetric measurements of the left and right temporal lobes and of cerebral oedema visible on T2 weighted Fluid Attenuated Inversion Recovery (FLAIR) images using stereology in conjunction with point counting. RESULTS: Temporal lobe volumes increased on average by 1.6% (standard deviation (SD 11%) in five patients who had not received corticosteroid therapy and decreased in two patients who had received corticosteroids by 8.5%. FLAIR hyperintensity volumes increased by 9% in patients not receiving treatment with corticosteroids and decreased by 29% in the two patients that had received corticosteroids. CONCLUSIONS: This study has shown it is feasible to quantify acute change in temporal lobe and total oedema volumes in HSV encephalitis and suggests a potential resolution of swelling in response to corticosteroid therapy. These techniques could be used as part of a randomized control trial to investigate the efficacy of corticosteroids for treating HSV encephalitis in conjunction with assessing clinical outcomes and could be of potential value in helping to predict the clinical outcomes of patients with HSV encephalitis

What traits are carried on mobile genetic elements, and why?

Although similar to any other organism, prokaryotes can transfer genes vertically from mother cell to daughter cell, they can also exchange certain genes horizontally. Genes can move within and between genomes at fast rates because of mobile genetic elements (MGEs). Although mobile elements are fundamentally self-interested entities, and thus replicate for their own gain, they frequently carry genes beneficial for their hosts and/or the neighbours of their hosts. Many genes that are carried by mobile elements code for traits that are expressed outside of the cell. Such traits are involved in bacterial sociality, such as the production of public goods, which benefit a cell's neighbours, or the production of bacteriocins, which harm a cell's neighbours. In this study we review the patterns that are emerging in the types of genes carried by mobile elements, and discuss the evolutionary and ecological conditions under which mobile elements evolve to carry their peculiar mix of parasitic, beneficial and cooperative genes

DNA repair systems and the pathogenesis of Mycobacterium tuberculosis: varying activities at different stages of infection

Mycobacteria, including most of all MTB (Mycobacterium tuberculosis), cause pathogenic infections in humans and, during the infectious process, are exposed to a range of environmental insults, including the host's immune response. From the moment MTB is exhaled by infected individuals, through an active and latent phase in the body of the new host, until the time they reach the reactivation stage, MTB is exposed to many types of DNA-damaging agents. Like all cellular organisms, MTB has efficient DNA repair systems, and these are believed to play essential roles in mycobacterial pathogenesis. As different stages of infection have great variation in the conditions in which mycobacteria reside, it is possible that different repair systems are essential for progression to specific phases of infection. MTB possesses homologues of DNA repair systems that are found widely in other species of bacteria, such as nucleotide excision repair, base excision repair and repair by homologous recombination. MTB also possesses a system for non-homologous end-joining of DNA breaks, which appears to be widespread in prokaryotes, although its presence is sporadic within different species within a genus. However, MTB does not possess homologues of the typical mismatch repair system that is found in most bacteria. Recent studies have demonstrated that DNA repair genes are expressed differentially at each stage of infection. In the present review, we focus on different DNA repair systems from mycobacteria and identify questions that remain in our understanding of how these systems have an impact upon the infection processes of these important pathogens

Multiwavelength Observations of Supersonic Plasma Blob Triggered by Reconnection Generated Velocity Pulse in AR10808

Using multi-wavelength observations of Solar and Heliospheric Observatory (SoHO)/Michelson Doppler Imager (MDI), Transition Region and Coronal Explorer (TRACE) 171 \AA, and H$\alpha$ from Culgoora Solar Observatory at Narrabri, Australia, we present a unique observational signature of a propagating supersonic plasma blob before an M6.2 class solar flare in AR10808 on 9th September 2005. The blob was observed between 05:27 UT to 05:32 UT with almost a constant shape for the first 2-3 minutes, and thereafter it quickly vanished in the corona. The observed lower bound speed of the blob is estimated as $\sim$215 km s$^{-1}$ in its dynamical phase. The evidence of the blob with almost similar shape and velocity concurrent in H$\alpha$ and TRACE 171 \AA\ supports its formation by multi-temperature plasma. The energy release by a recurrent 3-D reconnection process via the separator dome below the magnetic null point, between the emerging flux and pre-existing field lines in the lower solar atmosphere, is found to be the driver of a radial velocity pulse outwards that accelerates this plasma blob in the solar atmosphere. In support of identification of the possible driver of the observed eruption, we solve the two-dimensional ideal magnetohydrodynamic equations numerically to simulate the observed supersonic plasma blob. The numerical modelling closely match the observed velocity, evolution of multi-temperature plasma, and quick vanishing of the blob found in the observations. Under typical coronal conditions, such blobs may also carry an energy flux of 7.0$\times10^{6}$ ergs cm$^{-2}$ s$^{-1}$ to re-balance the coronal losses above active regions.Comment: Solar Physics; 22 Pages; 8 Figure

An optimal control theory approach to non-pharmaceutical interventions

<p>Abstract</p> <p>Background</p> <p>Non-pharmaceutical interventions (NPI) are the first line of defense against pandemic influenza. These interventions dampen virus spread by reducing contact between infected and susceptible persons. Because they curtail essential societal activities, they must be applied judiciously. Optimal control theory is an approach for modeling and balancing competing objectives such as epidemic spread and NPI cost.</p> <p>Methods</p> <p>We apply optimal control on an epidemiologic compartmental model to develop triggers for NPI implementation. The objective is to minimize expected person-days lost from influenza related deaths and NPI implementations for the model. We perform a multivariate sensitivity analysis based on Latin Hypercube Sampling to study the effects of input parameters on the optimal control policy. Additional studies investigated the effects of departures from the modeling assumptions, including exponential terminal time and linear NPI implementation cost.</p> <p>Results</p> <p>An optimal policy is derived for the control model using a linear NPI implementation cost. Linear cost leads to a "bang-bang" policy in which NPIs are applied at maximum strength when certain state criteria are met. Multivariate sensitivity analyses are presented which indicate that NPI cost, death rate, and recovery rate are influential in determining the policy structure. Further death rate, basic reproductive number and recovery rate are the most influential in determining the expected cumulative death. When applying the NPI policy, the cumulative deaths under exponential and gamma terminal times are close, which implies that the outcome of applying the "bang-bang" policy is insensitive to the exponential assumption. Quadratic cost leads to a multi-level policy in which NPIs are applied at varying strength levels, again based on certain state criteria. Results indicate that linear cost leads to more costly implementation resulting in fewer deaths.</p> <p>Conclusions</p> <p>The application of optimal control theory can provide valuable insight to developing effective control strategies for pandemic. Our findings highlight the importance of establishing a sensitive and timely surveillance system for pandemic preparedness.</p